Genetic_Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f04e5389",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c6fe70b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9a6f6f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>EncounterId</th>\n",
       "      <th>DischargeDisposision</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>DiabetesMellitus</th>\n",
       "      <th>ChronicKidneyDisease</th>\n",
       "      <th>Anemia</th>\n",
       "      <th>Depression</th>\n",
       "      <th>ChronicObstructivePulmonaryDisease</th>\n",
       "      <th>Age</th>\n",
       "      <th>ChronicDiseaseCount</th>\n",
       "      <th>LengthOfStay</th>\n",
       "      <th>EmergencyVisit</th>\n",
       "      <th>InpatientVisit</th>\n",
       "      <th>OutpatientVisit</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>BMIMin</th>\n",
       "      <th>BMIMax</th>\n",
       "      <th>BMIMedian</th>\n",
       "      <th>BMIMean</th>\n",
       "      <th>BPDiastolicMin</th>\n",
       "      <th>BPDiastolicMax</th>\n",
       "      <th>BPDiastolicMedian</th>\n",
       "      <th>BPDiastolicMean</th>\n",
       "      <th>BPSystolicMin</th>\n",
       "      <th>BPSystolicMax</th>\n",
       "      <th>BPSystolicMedian</th>\n",
       "      <th>BPSystolicMean</th>\n",
       "      <th>TemperatureMin</th>\n",
       "      <th>TemperatureMax</th>\n",
       "      <th>TemperatureMedian</th>\n",
       "      <th>TemperatureMean</th>\n",
       "      <th>HeartRateMin</th>\n",
       "      <th>HeartRateMax</th>\n",
       "      <th>HeartRateMedian</th>\n",
       "      <th>HeartRateMean</th>\n",
       "      <th>PulseRateMin</th>\n",
       "      <th>PulseRateMax</th>\n",
       "      <th>PulseRateMedian</th>\n",
       "      <th>PulseRateMean</th>\n",
       "      <th>RespiratoryRateMin</th>\n",
       "      <th>RespiratoryRateMax</th>\n",
       "      <th>RespiratoryRateMedian</th>\n",
       "      <th>RespiratoryRateMean</th>\n",
       "      <th>ACEInhibitors</th>\n",
       "      <th>ARBs</th>\n",
       "      <th>BetaBlockers</th>\n",
       "      <th>Diuretics</th>\n",
       "      <th>TotalMedicine</th>\n",
       "      <th>CardiacTroponin</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>SerumSodium</th>\n",
       "      <th>SerumCreatinine</th>\n",
       "      <th>BNP</th>\n",
       "      <th>NT-proBNP</th>\n",
       "      <th>ReadmissionWithin_90Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4200412</td>\n",
       "      <td>199171333</td>\n",
       "      <td>Home</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>DM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anemia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COPD</td>\n",
       "      <td>58</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>51.48</td>\n",
       "      <td>51.48</td>\n",
       "      <td>51.48</td>\n",
       "      <td>51.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4055894</td>\n",
       "      <td>26704337</td>\n",
       "      <td>Home</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>DM</td>\n",
       "      <td>CKD</td>\n",
       "      <td>Anemia</td>\n",
       "      <td>Depression</td>\n",
       "      <td>COPD</td>\n",
       "      <td>80</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>31.74</td>\n",
       "      <td>31.74</td>\n",
       "      <td>31.74</td>\n",
       "      <td>31.74</td>\n",
       "      <td>46</td>\n",
       "      <td>78</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.052632</td>\n",
       "      <td>78</td>\n",
       "      <td>150</td>\n",
       "      <td>106.0</td>\n",
       "      <td>109.080000</td>\n",
       "      <td>97.30</td>\n",
       "      <td>99.3</td>\n",
       "      <td>98.25</td>\n",
       "      <td>98.266667</td>\n",
       "      <td>57</td>\n",
       "      <td>89</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.833333</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4867407</td>\n",
       "      <td>60388216</td>\n",
       "      <td>Home</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>DM</td>\n",
       "      <td>CKD</td>\n",
       "      <td>Anemia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COPD</td>\n",
       "      <td>63</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.00000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4058064</td>\n",
       "      <td>274642265</td>\n",
       "      <td>Hospice - Home</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>DM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anemia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COPD</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20.55</td>\n",
       "      <td>20.55</td>\n",
       "      <td>20.55</td>\n",
       "      <td>20.55</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115.0</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>182.0</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>97.70</td>\n",
       "      <td>97.7</td>\n",
       "      <td>97.70</td>\n",
       "      <td>97.700000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4150623</td>\n",
       "      <td>70000001557327</td>\n",
       "      <td>SNF</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anemia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COPD</td>\n",
       "      <td>85</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38</td>\n",
       "      <td>72</td>\n",
       "      <td>60.0</td>\n",
       "      <td>59.044118</td>\n",
       "      <td>91</td>\n",
       "      <td>159</td>\n",
       "      <td>122.5</td>\n",
       "      <td>124.863636</td>\n",
       "      <td>96.98</td>\n",
       "      <td>98.6</td>\n",
       "      <td>97.88</td>\n",
       "      <td>97.854286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.47619</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PatientId     EncounterId DischargeDisposision  Gender   Race  \\\n",
       "0    4200412       199171333                 Home    Male  White   \n",
       "1    4055894        26704337                 Home    Male  White   \n",
       "2    4867407        60388216                 Home    Male  White   \n",
       "3    4058064       274642265       Hospice - Home  Female  White   \n",
       "4    4150623  70000001557327                  SNF  Female  White   \n",
       "\n",
       "  DiabetesMellitus ChronicKidneyDisease  Anemia  Depression   \\\n",
       "0               DM                  NaN  Anemia          NaN   \n",
       "1               DM                  CKD  Anemia  Depression    \n",
       "2               DM                  CKD  Anemia          NaN   \n",
       "3               DM                  NaN  Anemia          NaN   \n",
       "4              NaN                  NaN  Anemia          NaN   \n",
       "\n",
       "  ChronicObstructivePulmonaryDisease  Age  ChronicDiseaseCount  LengthOfStay  \\\n",
       "0                               COPD   58                   18             3   \n",
       "1                               COPD   80                   24             6   \n",
       "2                               COPD   63                   11             8   \n",
       "3                               COPD   73                    8             4   \n",
       "4                               COPD   85                   20             3   \n",
       "\n",
       "   EmergencyVisit  InpatientVisit  OutpatientVisit  TotalVisits  BMIMin  \\\n",
       "0              16               8               21           45   51.48   \n",
       "1               7               6                0           13   31.74   \n",
       "2               3               2                0            5    0.00   \n",
       "3               8               4                0           12   20.55   \n",
       "4               5               8                0           13    0.00   \n",
       "\n",
       "   BMIMax  BMIMedian  BMIMean  BPDiastolicMin  BPDiastolicMax  \\\n",
       "0   51.48      51.48    51.48               0               0   \n",
       "1   31.74      31.74    31.74              46              78   \n",
       "2    0.00       0.00     0.00               0               0   \n",
       "3   20.55      20.55    20.55             115             115   \n",
       "4    0.00       0.00     0.00              38              72   \n",
       "\n",
       "   BPDiastolicMedian  BPDiastolicMean  BPSystolicMin  BPSystolicMax  \\\n",
       "0                0.0         0.000000              0              0   \n",
       "1               63.0        63.052632             78            150   \n",
       "2                0.0         0.000000              0              0   \n",
       "3              115.0       115.000000            182            182   \n",
       "4               60.0        59.044118             91            159   \n",
       "\n",
       "   BPSystolicMedian  BPSystolicMean  TemperatureMin  TemperatureMax  \\\n",
       "0               0.0        0.000000            0.00             0.0   \n",
       "1             106.0      109.080000           97.30            99.3   \n",
       "2               0.0        0.000000            0.00             0.0   \n",
       "3             182.0      182.000000           97.70            97.7   \n",
       "4             122.5      124.863636           96.98            98.6   \n",
       "\n",
       "   TemperatureMedian  TemperatureMean  HeartRateMin  HeartRateMax  \\\n",
       "0               0.00         0.000000             0             0   \n",
       "1              98.25        98.266667            57            89   \n",
       "2               0.00         0.000000             0             0   \n",
       "3              97.70        97.700000             0             0   \n",
       "4              97.88        97.854286             0             0   \n",
       "\n",
       "   HeartRateMedian  HeartRateMean  PulseRateMin  PulseRateMax  \\\n",
       "0              0.0            0.0             0             0   \n",
       "1             68.0           68.0             0             0   \n",
       "2              0.0            0.0             0             0   \n",
       "3              0.0            0.0             0             0   \n",
       "4              0.0            0.0            71           110   \n",
       "\n",
       "   PulseRateMedian  PulseRateMean  RespiratoryRateMin  RespiratoryRateMax  \\\n",
       "0              0.0        0.00000                   0                   0   \n",
       "1              0.0        0.00000                  14                  22   \n",
       "2              0.0        0.00000                   0                   0   \n",
       "3              0.0        0.00000                  22                  22   \n",
       "4             93.0       91.47619                  14                  20   \n",
       "\n",
       "   RespiratoryRateMedian  RespiratoryRateMean  ACEInhibitors  ARBs  \\\n",
       "0                    0.0             0.000000              0     0   \n",
       "1                   17.5            17.833333              2     0   \n",
       "2                    0.0             0.000000              0     0   \n",
       "3                   22.0            22.000000              0     0   \n",
       "4                   17.0            17.000000              0     0   \n",
       "\n",
       "   BetaBlockers  Diuretics  TotalMedicine  CardiacTroponin  Hemoglobin  \\\n",
       "0             0          0              0              0.0        0.00   \n",
       "1             1          5              8              0.0        0.00   \n",
       "2             1          1              2              0.0       10.20   \n",
       "3             0          0              0              0.0        0.00   \n",
       "4             0          0              0              0.0        7.26   \n",
       "\n",
       "   SerumSodium  SerumCreatinine  BNP  NT-proBNP ReadmissionWithin_90Days  \n",
       "0          0.0             0.00  0.0        0.0                      Yes  \n",
       "1          0.0             1.54  0.0        0.0                       No  \n",
       "2          0.0             0.00  0.0        0.0                       No  \n",
       "3        132.0             0.00  0.0        0.0                       No  \n",
       "4          0.0             0.00  0.0        0.0                       No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Loading xlsx file and droping first row '''\n",
    "df = pd.read_excel(r\"Training_Data.xlsx\", header=[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07f348e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8481, 57)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "900a02ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''droping rows of duplicate id'''\n",
    "df = df.drop_duplicates('PatientId', keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5721e49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df.isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbe7edbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 Race 37\n",
      "5 DiabetesMellitus 2197\n",
      "6 ChronicKidneyDisease 2318\n",
      "7 Anemia 1892\n",
      "8 Depression  2963\n",
      "9 ChronicObstructivePulmonaryDisease 2317\n"
     ]
    }
   ],
   "source": [
    "'''checking columns with NAN values'''\n",
    "for i,(j,k) in  enumerate(df.isnull().sum().iteritems()):\n",
    "    if k:\n",
    "        print(i,j,k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3acd149",
   "metadata": {},
   "source": [
    "Filling Nan values in a Race column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a876e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                                        3911\n",
       "Black or African American                     368\n",
       "Other Race                                    195\n",
       "Asian                                          28\n",
       "American Indian or Alaska Native               24\n",
       "UnKnown                                        18\n",
       "Native Hawaiian or Other Pacific Islander       1\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d071013b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Race'] = df['Race'].fillna('UnKnown')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b058c7e",
   "metadata": {},
   "source": [
    "Filling Nan values in a DiabetesMellitus column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2bf3e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DM    2385\n",
       "Name: DiabetesMellitus, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DiabetesMellitus'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88059330",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DiabetesMellitus'] = df['DiabetesMellitus'].fillna('UnKnown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b96c75ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DiabetesMellitus'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79554e42",
   "metadata": {},
   "source": [
    "Filling Nan values in a ChronicKidneyDisease column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ffc78c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CKD    2264\n",
       "Name: ChronicKidneyDisease, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ChronicKidneyDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfb8d06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ChronicKidneyDisease'] = df['ChronicKidneyDisease'].fillna('UnKnown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0fceb7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ChronicKidneyDisease'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc015dd",
   "metadata": {},
   "source": [
    "Filling Nan values in a Anemia column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e9d2c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Anemia    2690\n",
       "Name: Anemia, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Anemia'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb5ecaa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Anemia'] = df['Anemia'].fillna('UnKnown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc9b9be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Anemia'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4ebe17",
   "metadata": {},
   "source": [
    "Filling Nan values in a Depression column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8c59f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Depression ': 'Depression'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ab32ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Depression     1619\n",
       "Name: Depression, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Depression'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0f250bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Depression'] = df['Depression'].fillna('UnKnown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "069afe64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Depression'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75587210",
   "metadata": {},
   "source": [
    "Filling Nan values in a ChronicObstructivePulmonaryDisease column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42bc449e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COPD    2265\n",
       "Name: ChronicObstructivePulmonaryDisease, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ChronicObstructivePulmonaryDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b15ccd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ChronicObstructivePulmonaryDisease'] = df['ChronicObstructivePulmonaryDisease'].fillna('UnKnown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ae2a8920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ChronicObstructivePulmonaryDisease'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6f273c",
   "metadata": {},
   "source": [
    "No more NaN value left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75812a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c918c13",
   "metadata": {},
   "source": [
    "Remaining Dtaset after Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "faf2c8b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4582, 57)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ad399e",
   "metadata": {},
   "source": [
    "Converting  y Label from 'yes', 'no' to numeric for of 1 and 0 respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dc37bcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df['ReadmissionWithin_90Days'])):\n",
    "    if df['ReadmissionWithin_90Days'].iloc[i]=='Yes':\n",
    "        df['ReadmissionWithin_90Days'].iloc[i]=1\n",
    "    elif df['ReadmissionWithin_90Days'].iloc[i]=='No':\n",
    "        df['ReadmissionWithin_90Days'].iloc[i]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bf55a41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= df['ReadmissionWithin_90Days']\n",
    "y=y.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e8862b",
   "metadata": {},
   "source": [
    "Function that takes dataset as input and return accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7697f639",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calaculate_accuracy(x):\n",
    "    X=pd.get_dummies(x)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.33, random_state=30)\n",
    "    lgt_R = LogisticRegression(random_state = 101, solver='lbfgs', multi_class='auto')\n",
    "    lgt_R.fit(X_train, y_train)\n",
    "    print('Accuracy:',lgt_R.score(X_test,y_test))\n",
    "    choromosomes_acc.append(lgt_R.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a5674a",
   "metadata": {},
   "source": [
    "create a list of all the columns name except the last column 'ReadmissionWithin_90Days' , which is assighned as label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "183d3625",
   "metadata": {},
   "outputs": [],
   "source": [
    "columnsName=df.drop(labels= 'ReadmissionWithin_90Days', axis= 1).columns.values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1d59d9",
   "metadata": {},
   "source": [
    "create 10 different chromosomes each having 30 different columns name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5da32559",
   "metadata": {},
   "outputs": [],
   "source": [
    "chromosomes=[]\n",
    "for i in range(10):\n",
    "    chromosomes.append(random.sample(columnsName, 30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ad0bb487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CardiacTroponin',\n",
       "  'NT-proBNP',\n",
       "  'HeartRateMax',\n",
       "  'HeartRateMin',\n",
       "  'BMIMax',\n",
       "  'Age',\n",
       "  'SerumSodium',\n",
       "  'BPDiastolicMean',\n",
       "  'BNP',\n",
       "  'RespiratoryRateMin',\n",
       "  'BMIMedian',\n",
       "  'Hemoglobin',\n",
       "  'Race',\n",
       "  'BPSystolicMin',\n",
       "  'BPDiastolicMin',\n",
       "  'Diuretics',\n",
       "  'RespiratoryRateMax',\n",
       "  'InpatientVisit',\n",
       "  'BMIMin',\n",
       "  'BPSystolicMedian',\n",
       "  'BPSystolicMax',\n",
       "  'ChronicKidneyDisease',\n",
       "  'Anemia',\n",
       "  'PatientId',\n",
       "  'EmergencyVisit',\n",
       "  'TotalMedicine',\n",
       "  'ACEInhibitors',\n",
       "  'ChronicDiseaseCount',\n",
       "  'TotalVisits',\n",
       "  'BPDiastolicMedian'],\n",
       " ['TemperatureMax',\n",
       "  'PulseRateMedian',\n",
       "  'EncounterId',\n",
       "  'BMIMedian',\n",
       "  'BPSystolicMax',\n",
       "  'BMIMin',\n",
       "  'BPSystolicMin',\n",
       "  'OutpatientVisit',\n",
       "  'BMIMax',\n",
       "  'BNP',\n",
       "  'Gender',\n",
       "  'TemperatureMin',\n",
       "  'ChronicKidneyDisease',\n",
       "  'TemperatureMedian',\n",
       "  'BetaBlockers',\n",
       "  'PatientId',\n",
       "  'HeartRateMedian',\n",
       "  'Anemia',\n",
       "  'SerumSodium',\n",
       "  'SerumCreatinine',\n",
       "  'BPDiastolicMin',\n",
       "  'TotalMedicine',\n",
       "  'ChronicObstructivePulmonaryDisease',\n",
       "  'RespiratoryRateMedian',\n",
       "  'PulseRateMin',\n",
       "  'PulseRateMax',\n",
       "  'HeartRateMax',\n",
       "  'DiabetesMellitus',\n",
       "  'ARBs',\n",
       "  'BPSystolicMean'],\n",
       " ['BPSystolicMedian',\n",
       "  'DischargeDisposision',\n",
       "  'RespiratoryRateMean',\n",
       "  'TemperatureMean',\n",
       "  'BetaBlockers',\n",
       "  'TotalVisits',\n",
       "  'RespiratoryRateMin',\n",
       "  'BMIMin',\n",
       "  'SerumSodium',\n",
       "  'BPDiastolicMin',\n",
       "  'Anemia',\n",
       "  'PulseRateMin',\n",
       "  'HeartRateMin',\n",
       "  'TemperatureMin',\n",
       "  'PulseRateMax',\n",
       "  'RespiratoryRateMedian',\n",
       "  'BPDiastolicMax',\n",
       "  'BMIMedian',\n",
       "  'ACEInhibitors',\n",
       "  'BMIMax',\n",
       "  'Race',\n",
       "  'InpatientVisit',\n",
       "  'OutpatientVisit',\n",
       "  'BPDiastolicMean',\n",
       "  'TotalMedicine',\n",
       "  'TemperatureMax',\n",
       "  'CardiacTroponin',\n",
       "  'DiabetesMellitus',\n",
       "  'HeartRateMean',\n",
       "  'BMIMean'],\n",
       " ['PulseRateMax',\n",
       "  'RespiratoryRateMin',\n",
       "  'TotalVisits',\n",
       "  'BMIMean',\n",
       "  'ChronicDiseaseCount',\n",
       "  'BetaBlockers',\n",
       "  'HeartRateMin',\n",
       "  'Diuretics',\n",
       "  'BPDiastolicMax',\n",
       "  'TemperatureMedian',\n",
       "  'InpatientVisit',\n",
       "  'NT-proBNP',\n",
       "  'RespiratoryRateMean',\n",
       "  'LengthOfStay',\n",
       "  'TemperatureMean',\n",
       "  'CardiacTroponin',\n",
       "  'Anemia',\n",
       "  'TemperatureMin',\n",
       "  'BPSystolicMin',\n",
       "  'HeartRateMean',\n",
       "  'BPSystolicMax',\n",
       "  'BPDiastolicMin',\n",
       "  'PulseRateMedian',\n",
       "  'PulseRateMin',\n",
       "  'BPDiastolicMedian',\n",
       "  'ARBs',\n",
       "  'BMIMin',\n",
       "  'Race',\n",
       "  'ACEInhibitors',\n",
       "  'Gender'],\n",
       " ['BPSystolicMin',\n",
       "  'BPDiastolicMean',\n",
       "  'EmergencyVisit',\n",
       "  'TemperatureMin',\n",
       "  'HeartRateMean',\n",
       "  'Anemia',\n",
       "  'DischargeDisposision',\n",
       "  'HeartRateMin',\n",
       "  'CardiacTroponin',\n",
       "  'RespiratoryRateMedian',\n",
       "  'TemperatureMean',\n",
       "  'TotalMedicine',\n",
       "  'LengthOfStay',\n",
       "  'DiabetesMellitus',\n",
       "  'Hemoglobin',\n",
       "  'PulseRateMean',\n",
       "  'PulseRateMin',\n",
       "  'ARBs',\n",
       "  'BMIMax',\n",
       "  'BPDiastolicMedian',\n",
       "  'NT-proBNP',\n",
       "  'BetaBlockers',\n",
       "  'BPDiastolicMin',\n",
       "  'BPSystolicMedian',\n",
       "  'HeartRateMedian',\n",
       "  'InpatientVisit',\n",
       "  'Diuretics',\n",
       "  'BNP',\n",
       "  'BMIMean',\n",
       "  'BMIMedian'],\n",
       " ['BPSystolicMin',\n",
       "  'Gender',\n",
       "  'PulseRateMean',\n",
       "  'ACEInhibitors',\n",
       "  'DiabetesMellitus',\n",
       "  'RespiratoryRateMax',\n",
       "  'SerumCreatinine',\n",
       "  'HeartRateMin',\n",
       "  'RespiratoryRateMedian',\n",
       "  'EmergencyVisit',\n",
       "  'HeartRateMedian',\n",
       "  'NT-proBNP',\n",
       "  'BMIMedian',\n",
       "  'ChronicDiseaseCount',\n",
       "  'LengthOfStay',\n",
       "  'CardiacTroponin',\n",
       "  'SerumSodium',\n",
       "  'Anemia',\n",
       "  'Depression',\n",
       "  'BMIMean',\n",
       "  'TemperatureMean',\n",
       "  'Race',\n",
       "  'EncounterId',\n",
       "  'TemperatureMin',\n",
       "  'HeartRateMean',\n",
       "  'BPSystolicMedian',\n",
       "  'PulseRateMax',\n",
       "  'DischargeDisposision',\n",
       "  'BPSystolicMax',\n",
       "  'RespiratoryRateMin'],\n",
       " ['SerumCreatinine',\n",
       "  'RespiratoryRateMax',\n",
       "  'HeartRateMean',\n",
       "  'Age',\n",
       "  'DischargeDisposision',\n",
       "  'BPSystolicMean',\n",
       "  'BetaBlockers',\n",
       "  'ARBs',\n",
       "  'PatientId',\n",
       "  'BMIMedian',\n",
       "  'HeartRateMedian',\n",
       "  'NT-proBNP',\n",
       "  'PulseRateMax',\n",
       "  'BNP',\n",
       "  'EncounterId',\n",
       "  'Gender',\n",
       "  'Depression',\n",
       "  'TemperatureMax',\n",
       "  'BPDiastolicMedian',\n",
       "  'RespiratoryRateMin',\n",
       "  'Hemoglobin',\n",
       "  'ChronicDiseaseCount',\n",
       "  'SerumSodium',\n",
       "  'BMIMean',\n",
       "  'OutpatientVisit',\n",
       "  'BPDiastolicMean',\n",
       "  'ACEInhibitors',\n",
       "  'LengthOfStay',\n",
       "  'EmergencyVisit',\n",
       "  'InpatientVisit'],\n",
       " ['TemperatureMax',\n",
       "  'BMIMean',\n",
       "  'NT-proBNP',\n",
       "  'ChronicDiseaseCount',\n",
       "  'PulseRateMedian',\n",
       "  'InpatientVisit',\n",
       "  'BMIMedian',\n",
       "  'BPDiastolicMedian',\n",
       "  'ChronicObstructivePulmonaryDisease',\n",
       "  'EncounterId',\n",
       "  'SerumSodium',\n",
       "  'OutpatientVisit',\n",
       "  'Gender',\n",
       "  'HeartRateMedian',\n",
       "  'BetaBlockers',\n",
       "  'PatientId',\n",
       "  'LengthOfStay',\n",
       "  'PulseRateMin',\n",
       "  'PulseRateMax',\n",
       "  'DischargeDisposision',\n",
       "  'HeartRateMin',\n",
       "  'BPSystolicMin',\n",
       "  'Diuretics',\n",
       "  'BPDiastolicMean',\n",
       "  'HeartRateMax',\n",
       "  'EmergencyVisit',\n",
       "  'TemperatureMean',\n",
       "  'Depression',\n",
       "  'Age',\n",
       "  'DiabetesMellitus'],\n",
       " ['ARBs',\n",
       "  'Depression',\n",
       "  'BMIMean',\n",
       "  'BMIMedian',\n",
       "  'BPDiastolicMax',\n",
       "  'ChronicDiseaseCount',\n",
       "  'Hemoglobin',\n",
       "  'Gender',\n",
       "  'BMIMax',\n",
       "  'Diuretics',\n",
       "  'DischargeDisposision',\n",
       "  'BPSystolicMean',\n",
       "  'TemperatureMean',\n",
       "  'ChronicObstructivePulmonaryDisease',\n",
       "  'TemperatureMin',\n",
       "  'HeartRateMax',\n",
       "  'CardiacTroponin',\n",
       "  'BPSystolicMax',\n",
       "  'BPDiastolicMin',\n",
       "  'PulseRateMax',\n",
       "  'OutpatientVisit',\n",
       "  'TemperatureMax',\n",
       "  'BPDiastolicMedian',\n",
       "  'BetaBlockers',\n",
       "  'HeartRateMedian',\n",
       "  'BMIMin',\n",
       "  'Race',\n",
       "  'TotalMedicine',\n",
       "  'EncounterId',\n",
       "  'RespiratoryRateMax'],\n",
       " ['InpatientVisit',\n",
       "  'RespiratoryRateMin',\n",
       "  'Anemia',\n",
       "  'ChronicKidneyDisease',\n",
       "  'Gender',\n",
       "  'TemperatureMedian',\n",
       "  'NT-proBNP',\n",
       "  'CardiacTroponin',\n",
       "  'BMIMedian',\n",
       "  'DiabetesMellitus',\n",
       "  'Diuretics',\n",
       "  'Age',\n",
       "  'BPDiastolicMedian',\n",
       "  'BPDiastolicMin',\n",
       "  'TemperatureMean',\n",
       "  'TotalVisits',\n",
       "  'SerumSodium',\n",
       "  'OutpatientVisit',\n",
       "  'Hemoglobin',\n",
       "  'BPDiastolicMax',\n",
       "  'HeartRateMin',\n",
       "  'BMIMax',\n",
       "  'ChronicObstructivePulmonaryDisease',\n",
       "  'BNP',\n",
       "  'PulseRateMedian',\n",
       "  'TemperatureMin',\n",
       "  'RespiratoryRateMedian',\n",
       "  'DischargeDisposision',\n",
       "  'PulseRateMean',\n",
       "  'BMIMin']]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chromosomes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56524eb",
   "metadata": {},
   "source": [
    "function to perform mutation that takes a list of columns are randomly select a column from dataframe and replace it with a column in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1a200d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutation(lst):\n",
    "    df_col=random.sample(columnsName, 1)\n",
    "    chrom_col=random.sample(lst, 1)\n",
    "    indx=lst.index(chrom_col[0])\n",
    "    lst[indx]=df_col[0]\n",
    "    return lst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3301f500",
   "metadata": {},
   "source": [
    "function to compare the current accuracies with the previous accuracies, so that to check out if the accuracy coming is constant or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c4b673be",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_ten_best_accuracies=[]\n",
    "def compare_acc():\n",
    "    v=np.diff(first_ten_best_accuracies)\n",
    "    if(all(i >=0.01 for i in v)):\n",
    "        return False\n",
    "    else:\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "db21effc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_over(lst1,lst2):\n",
    "    rndmIndx=random.randint(0,29)\n",
    "    lst1=lst1[:rndmIndx]+lst2[rndmIndx:]\n",
    "    lst2=lst2[:rndmIndx]+lst1[rndmIndx:]\n",
    "    return(lst1,lst2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1598ff7",
   "metadata": {},
   "source": [
    "Genetic Alogorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ac35f745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.82088565763384\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8241903502974224\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  6\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 1 Ended.........\n",
      "\n",
      "Iteration :  2\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.82088565763384\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8228684732319894\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  6\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 2 Ended.........\n",
      "\n",
      "Iteration :  3\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.82088565763384\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.82088565763384\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  7\n",
      "Second Maximum Accuracy chromosome is  3\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 3 Ended.........\n",
      "\n",
      "Iteration :  4\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.82088565763384\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  7\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 4 Ended.........\n",
      "\n",
      "Iteration :  5\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8215465961665566\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8189028420356906\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  8\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 5 Ended.........\n",
      "\n",
      "Iteration :  6\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8189028420356906\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  7\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 6 Ended.........\n",
      "\n",
      "Iteration :  7\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8215465961665566\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  7\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 7 Ended.........\n",
      "\n",
      "Iteration :  8\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8228684732319894\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  7\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 8 Ended.........\n",
      "\n",
      "Iteration :  9\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8235294117647058\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  8\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 9 Ended.........\n",
      "\n",
      "Iteration :  10\n",
      "----------------------------------------------\n",
      "For chromosome 1 :\n",
      "Accuracy: 0.8182419035029742\n",
      "For chromosome 2 :\n",
      "Accuracy: 0.8155981493721084\n",
      "For chromosome 3 :\n",
      "Accuracy: 0.8175809649702578\n",
      "For chromosome 4 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 5 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 6 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 7 :\n",
      "Accuracy: 0.8195637805684072\n",
      "For chromosome 8 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 9 :\n",
      "Accuracy: 0.8202247191011236\n",
      "For chromosome 10 :\n",
      "Accuracy: 0.8202247191011236\n",
      "\n",
      "Maximum Accuracy chromosome is  5\n",
      "Second Maximum Accuracy chromosome is  4\n",
      "\n",
      "Applying  Crossover and Mutation Between the selected Top chromosomes..............\n",
      "\n",
      "Replacing the selected chromosomes with the new chromosomes........\n",
      "\n",
      "Iteration : 10 Ended.........\n",
      "\n",
      "\n",
      "Maximum Accuracy is :  0.8202247191011236\n",
      "Best columns for Maximum accuracy are:\n",
      "  ['CardiacTroponin', 'SerumCreatinine', 'LengthOfStay', 'ChronicDiseaseCount', 'PulseRateMedian', 'InpatientVisit', 'BMIMedian', 'BPDiastolicMedian', 'ChronicObstructivePulmonaryDisease', 'EncounterId', 'SerumSodium', 'OutpatientVisit', 'Gender', 'SerumSodium', 'BetaBlockers', 'Depression', 'RespiratoryRateMax', 'BNP', 'ACEInhibitors', 'BPSystolicMedian', 'TemperatureMean', 'TotalMedicine', 'EncounterId', 'BPDiastolicMean', 'BPSystolicMin', 'BPSystolicMedian', 'PulseRateMax', 'Depression', 'BPSystolicMean', 'HeartRateMax']\n"
     ]
    }
   ],
   "source": [
    "epochs=1000\n",
    "j=1\n",
    "flag=0\n",
    "while(epochs):\n",
    "#     after 10 iterations ,the record of previous max accuracies is checked,\n",
    "# weather the accuracies are constant or changing ,if constant than terminate,if increasing than continue\n",
    "    if flag==10:\n",
    "        if(compare_acc()):\n",
    "            print(\"\\nMaximum Accuracy is : \",largest)\n",
    "            print(\"Best columns for Maximum accuracy are:\\n \",chromosomes[largest_chromosomes_indx])\n",
    "            break\n",
    "        else:\n",
    "            flag=0\n",
    "    print('Iteration : ',j)\n",
    "    print('----------------------------------------------')\n",
    "#     calling calaculate_accuracy function and pass it each chromosomes to calculate accuracies and store it in alist\n",
    "    cnt=1\n",
    "    best_two_accuracy=()\n",
    "    choromosomes_acc=[]\n",
    "    for i in chromosomes:\n",
    "        tempdf=df[i]\n",
    "        print('For chromosome ' + str(cnt) + ' :')\n",
    "        cnt+=1\n",
    "        calaculate_accuracy(tempdf)\n",
    "#     Get two chromosomes having maximum accuracies\n",
    "    sort = sorted(choromosomes_acc)\n",
    "    sort=list(set(sort))\n",
    "    largest = sort[-1]\n",
    "    second_largest = sort[-2]\n",
    "    largest_chromosomes_indx=choromosomes_acc.index(largest)\n",
    "    second_largest_chromosomes_indx=choromosomes_acc.index(second_largest)\n",
    "    print('\\nMaximum Accuracy chromosome is ' ,largest_chromosomes_indx+1)\n",
    "    print('Second Maximum Accuracy chromosome is ' ,second_largest_chromosomes_indx+1)\n",
    "    first_ten_best_accuracies.append(largest)\n",
    "#     apply crossover between top two selected chromosomes\n",
    "    tpl=cross_over(chromosomes[largest_chromosomes_indx],chromosomes[second_largest_chromosomes_indx])\n",
    "    print(\"\\nApplying  Crossover and Mutation Between the selected Top chromosomes..............\\n\")\n",
    "    newChromosome1=tpl[0]\n",
    "    newChromosome2=tpl[1]\n",
    "#     calling mutation function to perform muttion\n",
    "    newChromosome1=mutation(newChromosome1)\n",
    "    newChromosome2=mutation(newChromosome2)\n",
    "    print('Replacing the selected chromosomes with the new chromosomes........\\n')\n",
    "    chromosomes[largest_chromosomes_indx]=newChromosome1\n",
    "    chromosomes[second_largest_chromosomes_indx]=newChromosome2\n",
    "    print('Iteration : '+ str(j)+ ' Ended.........\\n')\n",
    "    epochs-=1\n",
    "    j+=1\n",
    "    flag+=1"
   ]
  }
 ],
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