Sleep apnea exploratory analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep apnea exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import seaborn as sns\n",
    "import time \n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "from sympy.logic.boolalg import Nor\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.decomposition import PCA\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>term</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>allergies</th>\n",
       "      <th>asthma</th>\n",
       "      <th>gerd</th>\n",
       "      <th>tonsilsize</th>\n",
       "      <th>nrem</th>\n",
       "      <th>...</th>\n",
       "      <th>tst50co2</th>\n",
       "      <th>zscore</th>\n",
       "      <th>ahi</th>\n",
       "      <th>oahi</th>\n",
       "      <th>cai</th>\n",
       "      <th>oai</th>\n",
       "      <th>ohi</th>\n",
       "      <th>lowsao2</th>\n",
       "      <th>peakc02</th>\n",
       "      <th>tb90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.929191</td>\n",
       "      <td>3.093771</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>75.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.71</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.462240</td>\n",
       "      <td>4.550308</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>80.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.927145</td>\n",
       "      <td>15.8</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.33</td>\n",
       "      <td>13.97</td>\n",
       "      <td>86</td>\n",
       "      <td>50</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.104691</td>\n",
       "      <td>4.678987</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>75.1</td>\n",
       "      <td>...</td>\n",
       "      <td>83.3</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>28.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.09</td>\n",
       "      <td>27.71</td>\n",
       "      <td>80</td>\n",
       "      <td>64</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.219425</td>\n",
       "      <td>16.953425</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>44.7</td>\n",
       "      <td>...</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.460000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>115.50</td>\n",
       "      <td>115.50</td>\n",
       "      <td>64</td>\n",
       "      <td>52</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.974365</td>\n",
       "      <td>17.120548</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>66.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.160000</td>\n",
       "      <td>60.5</td>\n",
       "      <td>42.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>16.90</td>\n",
       "      <td>16.90</td>\n",
       "      <td>85</td>\n",
       "      <td>47</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender ethnicity  term        bmi        age allergies asthma gerd  \\\n",
       "0   Male     White   0.0  25.929191   3.093771       Yes    Yes   No   \n",
       "1   Male  Hispanic   0.0  31.462240   4.550308        No     No   No   \n",
       "2   Male  Hispanic   0.0  36.104691   4.678987       Yes    Yes   No   \n",
       "3   Male     Black   0.0  69.219425  16.953425        No     No   No   \n",
       "4   Male     Black   0.0  50.974365  17.120548        No     No   No   \n",
       "\n",
       "   tonsilsize  nrem  ...   tst50co2    zscore    ahi  oahi  cai     oai  \\\n",
       "0           3  75.7  ...        0.0  4.920000    2.1   2.0  0.1    0.29   \n",
       "1           3  80.6  ...        0.0  4.927145   15.8  15.3  0.5    1.33   \n",
       "2           2  75.1  ...       83.3  4.990000   28.6  27.8  0.8    0.09   \n",
       "3           3  44.7  ...       12.4  3.460000  118.0   2.5  0.5  115.50   \n",
       "4           3  66.5  ...        0.0  3.160000   60.5  42.7  1.4   16.90   \n",
       "\n",
       "      ohi  lowsao2  peakc02  tb90  \n",
       "0    1.71       90       50   0.0  \n",
       "1   13.97       86       50   0.3  \n",
       "2   27.71       80       64   2.2  \n",
       "3  115.50       64       52  34.2  \n",
       "4   16.90       85       47   2.5  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load csv file \n",
    "load_file = '/Users/farahshamout/Desktop/RAIL/data.csv'\n",
    "data = pd.read_csv(load_file)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>tonsilsize</th>\n",
       "      <th>nrem</th>\n",
       "      <th>rem</th>\n",
       "      <th>sleepefficiency</th>\n",
       "      <th>arousali</th>\n",
       "      <th>tst50co2</th>\n",
       "      <th>zscore</th>\n",
       "      <th>ahi</th>\n",
       "      <th>oahi</th>\n",
       "      <th>cai</th>\n",
       "      <th>oai</th>\n",
       "      <th>ohi</th>\n",
       "      <th>lowsao2</th>\n",
       "      <th>peakc02</th>\n",
       "      <th>tb90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>439.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>456.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.102506</td>\n",
       "      <td>26.863047</td>\n",
       "      <td>10.508655</td>\n",
       "      <td>2.385965</td>\n",
       "      <td>72.708991</td>\n",
       "      <td>16.980702</td>\n",
       "      <td>81.726096</td>\n",
       "      <td>15.389254</td>\n",
       "      <td>7.815132</td>\n",
       "      <td>1.590278</td>\n",
       "      <td>11.858553</td>\n",
       "      <td>8.698684</td>\n",
       "      <td>0.664474</td>\n",
       "      <td>3.896996</td>\n",
       "      <td>5.417390</td>\n",
       "      <td>88.046053</td>\n",
       "      <td>48.298246</td>\n",
       "      <td>1.407018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.303658</td>\n",
       "      <td>10.561774</td>\n",
       "      <td>4.759669</td>\n",
       "      <td>0.942373</td>\n",
       "      <td>35.218159</td>\n",
       "      <td>6.601057</td>\n",
       "      <td>13.825466</td>\n",
       "      <td>15.924183</td>\n",
       "      <td>19.326153</td>\n",
       "      <td>1.097054</td>\n",
       "      <td>23.614481</td>\n",
       "      <td>16.209962</td>\n",
       "      <td>1.964702</td>\n",
       "      <td>15.446075</td>\n",
       "      <td>16.264809</td>\n",
       "      <td>7.116616</td>\n",
       "      <td>6.134650</td>\n",
       "      <td>6.196213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.161674</td>\n",
       "      <td>2.058864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.135029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.922654</td>\n",
       "      <td>5.823409</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.933396</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.472229</td>\n",
       "      <td>12.073973</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.600000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>85.450000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.883140</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.120368</td>\n",
       "      <td>14.447945</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.900000</td>\n",
       "      <td>21.125000</td>\n",
       "      <td>91.625000</td>\n",
       "      <td>16.125000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.416769</td>\n",
       "      <td>11.225000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.592500</td>\n",
       "      <td>3.720000</td>\n",
       "      <td>92.250000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.797430</td>\n",
       "      <td>17.954826</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>793.700000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>128.800000</td>\n",
       "      <td>99.600000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>187.700000</td>\n",
       "      <td>124.700000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             term         bmi         age  tonsilsize        nrem         rem  \\\n",
       "count  439.000000  456.000000  456.000000  456.000000  456.000000  456.000000   \n",
       "mean     0.102506   26.863047   10.508655    2.385965   72.708991   16.980702   \n",
       "std      0.303658   10.561774    4.759669    0.942373   35.218159    6.601057   \n",
       "min      0.000000   14.161674    2.058864    1.000000   22.800000    0.000000   \n",
       "25%      0.000000   17.922654    5.823409    2.000000   68.000000   13.400000   \n",
       "50%      0.000000   24.472229   12.073973    2.000000   72.600000   17.800000   \n",
       "75%      0.000000   33.120368   14.447945    3.000000   76.900000   21.125000   \n",
       "max      1.000000   72.797430   17.954826    4.000000  793.700000   31.100000   \n",
       "\n",
       "       sleepefficiency    arousali    tst50co2      zscore         ahi  \\\n",
       "count       456.000000  456.000000  456.000000  456.000000  456.000000   \n",
       "mean         81.726096   15.389254    7.815132    1.590278   11.858553   \n",
       "std          13.825466   15.924183   19.326153    1.097054   23.614481   \n",
       "min           8.300000    1.600000    0.000000   -1.135029    0.000000   \n",
       "25%          75.700000    7.800000    0.000000    0.933396    0.900000   \n",
       "50%          85.450000   10.850000    0.000000    1.883140    3.400000   \n",
       "75%          91.625000   16.125000    1.400000    2.416769   11.225000   \n",
       "max          99.000000  128.800000   99.600000    4.990000  187.700000   \n",
       "\n",
       "             oahi         cai         oai         ohi     lowsao2     peakc02  \\\n",
       "count  456.000000  456.000000  456.000000  456.000000  456.000000  456.000000   \n",
       "mean     8.698684    0.664474    3.896996    5.417390   88.046053   48.298246   \n",
       "std     16.209962    1.964702   15.446075   16.264809    7.116616    6.134650   \n",
       "min      0.000000    0.000000   -0.200000    0.000000   51.000000   32.000000   \n",
       "25%      0.600000    0.000000    0.000000    0.000000   86.000000   44.000000   \n",
       "50%      2.600000    0.200000    0.200000    0.850000   90.000000   48.000000   \n",
       "75%      9.400000    0.600000    1.592500    3.720000   92.250000   52.000000   \n",
       "max    124.700000   31.000000  188.500000  188.500000   98.000000   80.000000   \n",
       "\n",
       "             tb90  \n",
       "count  456.000000  \n",
       "mean     1.407018  \n",
       "std      6.196213  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      0.225000  \n",
       "max     64.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>term</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>allergies</th>\n",
       "      <th>asthma</th>\n",
       "      <th>gerd</th>\n",
       "      <th>tonsilsize</th>\n",
       "      <th>nrem</th>\n",
       "      <th>...</th>\n",
       "      <th>tst50co2</th>\n",
       "      <th>zscore</th>\n",
       "      <th>ahi</th>\n",
       "      <th>oahi</th>\n",
       "      <th>cai</th>\n",
       "      <th>oai</th>\n",
       "      <th>ohi</th>\n",
       "      <th>lowsao2</th>\n",
       "      <th>peakc02</th>\n",
       "      <th>tb90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.929191</td>\n",
       "      <td>3.093771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>75.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.71</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.462240</td>\n",
       "      <td>4.550308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>80.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.927145</td>\n",
       "      <td>15.8</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.33</td>\n",
       "      <td>13.97</td>\n",
       "      <td>86</td>\n",
       "      <td>50</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.104691</td>\n",
       "      <td>4.678987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>75.1</td>\n",
       "      <td>...</td>\n",
       "      <td>83.3</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>28.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.09</td>\n",
       "      <td>27.71</td>\n",
       "      <td>80</td>\n",
       "      <td>64</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.219425</td>\n",
       "      <td>16.953425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>44.7</td>\n",
       "      <td>...</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.460000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>115.50</td>\n",
       "      <td>115.50</td>\n",
       "      <td>64</td>\n",
       "      <td>52</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.974365</td>\n",
       "      <td>17.120548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>66.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.160000</td>\n",
       "      <td>60.5</td>\n",
       "      <td>42.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>16.90</td>\n",
       "      <td>16.90</td>\n",
       "      <td>85</td>\n",
       "      <td>47</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ethnicity  term        bmi        age  allergies  asthma  gerd  \\\n",
       "0     0.0        1.0   0.0  25.929191   3.093771        1.0     1.0   0.0   \n",
       "1     0.0        2.0   0.0  31.462240   4.550308        0.0     0.0   0.0   \n",
       "2     0.0        2.0   0.0  36.104691   4.678987        1.0     1.0   0.0   \n",
       "3     0.0        3.0   0.0  69.219425  16.953425        0.0     0.0   0.0   \n",
       "4     0.0        3.0   0.0  50.974365  17.120548        0.0     0.0   0.0   \n",
       "\n",
       "   tonsilsize  nrem  ...   tst50co2    zscore    ahi  oahi  cai     oai  \\\n",
       "0           3  75.7  ...        0.0  4.920000    2.1   2.0  0.1    0.29   \n",
       "1           3  80.6  ...        0.0  4.927145   15.8  15.3  0.5    1.33   \n",
       "2           2  75.1  ...       83.3  4.990000   28.6  27.8  0.8    0.09   \n",
       "3           3  44.7  ...       12.4  3.460000  118.0   2.5  0.5  115.50   \n",
       "4           3  66.5  ...        0.0  3.160000   60.5  42.7  1.4   16.90   \n",
       "\n",
       "      ohi  lowsao2  peakc02  tb90  \n",
       "0    1.71       90       50   0.0  \n",
       "1   13.97       86       50   0.3  \n",
       "2   27.71       80       64   2.2  \n",
       "3  115.50       64       52  34.2  \n",
       "4   16.90       85       47   2.5  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove all text from data (i.e. binarize Yes/No, female/Male, and categorize ethnicity)\n",
    "# Parse through data and change variables as follows:\n",
    "    # gender: 1 = female, 0 = male\n",
    "    # ethnicity: 1 = White, 2 = Hispanic, 3 = Black, 4 = Asian\n",
    "    # Allergies: 1 = Yes, 0 = No\n",
    "    # Asthma: 1 = Yes, 0 = No\n",
    "    # Gerd: 1 = Yes, 0 = No\n",
    "\n",
    "# Term contains Nan value so take all rows where term is not NaN\n",
    "data = data[np.isfinite(data['term'])]\n",
    "\n",
    "# Replace strings with binary values / disrete values for multivalued columns\n",
    "obs = data.replace(to_replace = ['Yes', 'No', 'Male','Female', 'White','Hispanic', 'Black', 'Asian']\n",
    "                   , value = [ 1.0,0.0,0.0,1.0,1.0,2.0,3.0,4.0])\n",
    "\n",
    "# Create a subset of obs that contains only initial data before sleep test\n",
    "cols = ['gender','ethnicity','term', 'bmi','age','allergies','asthma','gerd','tonsilsize', 'ahi']\n",
    "init_obs = obs\n",
    "\n",
    "# DATA SUMMARY:\n",
    "    # data with original data \n",
    "    # obs with all numerical values\n",
    "    # init_obs with initial observations before sleep-test i.e. variables from gender to tonsilsize\n",
    "init_obs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>All</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>% of patient cohort</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>47.608200</td>\n",
       "      <td>52.391800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average age</th>\n",
       "      <td>10.641929</td>\n",
       "      <td>10.905582</td>\n",
       "      <td>10.402349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% OSA (ahi&gt;5)</th>\n",
       "      <td>41.002278</td>\n",
       "      <td>36.842105</td>\n",
       "      <td>44.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%allergies</th>\n",
       "      <td>35.990888</td>\n",
       "      <td>38.277512</td>\n",
       "      <td>33.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%allergies AND OSA</th>\n",
       "      <td>14.806378</td>\n",
       "      <td>14.832536</td>\n",
       "      <td>14.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%asthma</th>\n",
       "      <td>28.929385</td>\n",
       "      <td>25.837321</td>\n",
       "      <td>31.739130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%asthma AND OSA</th>\n",
       "      <td>11.161731</td>\n",
       "      <td>8.612440</td>\n",
       "      <td>13.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%gerd</th>\n",
       "      <td>7.289294</td>\n",
       "      <td>6.220096</td>\n",
       "      <td>8.260870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% gerd AND OSA</th>\n",
       "      <td>2.050114</td>\n",
       "      <td>1.913876</td>\n",
       "      <td>2.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% AAG</th>\n",
       "      <td>3.416856</td>\n",
       "      <td>3.349282</td>\n",
       "      <td>3.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% AAG AND OSA</th>\n",
       "      <td>0.911162</td>\n",
       "      <td>0.956938</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            All    Females      Males\n",
       "% of patient cohort  100.000000  47.608200  52.391800\n",
       "Average age           10.641929  10.905582  10.402349\n",
       "% OSA (ahi>5)         41.002278  36.842105  44.782609\n",
       "%allergies            35.990888  38.277512  33.913043\n",
       "%allergies AND OSA    14.806378  14.832536  14.782609\n",
       "%asthma               28.929385  25.837321  31.739130\n",
       "%asthma AND OSA       11.161731   8.612440  13.478261\n",
       "%gerd                  7.289294   6.220096   8.260870\n",
       "% gerd AND OSA         2.050114   1.913876   2.173913\n",
       "% AAG                  3.416856   3.349282   3.478261\n",
       "% AAG AND OSA          0.911162   0.956938   0.869565"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initial statistics i.e. gender, age, and investigations of asthma, allergies and gerd \n",
    "# Note that:\n",
    "    #AAG refers to asthma AND allergies AND gerd\n",
    "    \n",
    "stats_labels = ['% of patient cohort', \n",
    "                'Average age', \n",
    "                '% OSA (ahi>5)', \n",
    "                '%allergies ',\n",
    "                '%allergies AND OSA',\n",
    "                '%asthma', \n",
    "                '%asthma AND OSA',\n",
    "                '%gerd ',\n",
    "                '% gerd AND OSA',\n",
    "                '% AAG',                \n",
    "                '% AAG AND OSA']\n",
    "data_male = data.loc[data['gender'] == 'Male']\n",
    "data_female = data.loc[data['gender'] == 'Female']\n",
    "init_stats_dict = {'All': [100, \n",
    "                           data['age'].mean(), \n",
    "                           len(data.loc[data['ahi']>5])*100/len(data),\n",
    "                           len(data.loc[data['allergies']=='Yes'])*100/len(data), \n",
    "                           len(data.loc[(data['allergies']=='Yes') & (data['ahi']>5)])*100/len(data), \n",
    "                           len(data.loc[data['asthma']=='Yes'])*100/len(data), \n",
    "                           len(data.loc[(data['asthma']=='Yes')& (data['ahi']>5)])*100/len(data), \n",
    "                           len(data.loc[data['gerd']=='Yes'])*100/len(data),\n",
    "                           len(data.loc[(data['gerd']=='Yes')& (data['ahi']>5)])*100/len(data),\n",
    "                           len(data.loc[(data['gerd']=='Yes')& (data['asthma']=='Yes') & (data['allergies']=='Yes')])*100/len(data),\n",
    "                           len(data.loc[(data['gerd']=='Yes')& (data['asthma']=='Yes') & (data['allergies']=='Yes') &(data['ahi']>5)])*100/len(data)], \n",
    "                   'Females':[len(data_female)*100/len(data), \n",
    "                            data_female['age'].mean(), \n",
    "                            len(data_female.loc[data_female['ahi']>5])*100/len(data_female),\n",
    "                            len(data_female.loc[data_female['allergies']=='Yes'])*100/len(data_female), \n",
    "                            len(data_female.loc[(data_female['allergies']=='Yes')& (data_female['ahi']>5)])*100/len(data_female),\n",
    "                            len(data_female.loc[data_female['asthma']=='Yes'])*100/len(data_female), \n",
    "                            len(data_female.loc[(data_female['asthma']=='Yes') & (data_female['ahi']>5)])*100/len(data_female), \n",
    "                            len(data_female.loc[data_female['gerd']=='Yes'])*100/len(data_female),\n",
    "                            len(data_female.loc[(data_female['gerd']=='Yes') & (data_female['ahi']>5)])*100/len(data_female),\n",
    "                            len(data_female.loc[(data_female['gerd']=='Yes')& (data_female['asthma']=='Yes') & (data_female['allergies']=='Yes')])*100/len(data_female), \n",
    "                            len(data_female.loc[(data_female['gerd']=='Yes')& (data_female['asthma']=='Yes') & (data_female['allergies']=='Yes') &(data_female['ahi']>5)])*100/len(data_female)], \n",
    "                   'Males':[len(data_male)*100/len(data), \n",
    "                            data_male['age'].mean(), \n",
    "                            len(data_male.loc[data_male['ahi']>5])*100/len(data_male),\n",
    "                            len(data_male.loc[data_male['allergies']=='Yes'])*100/len(data_male), \n",
    "                            len(data_male.loc[(data_male['allergies']=='Yes')& (data_male['ahi']>5)])*100/len(data_male), \n",
    "                            len(data_male.loc[data_male['asthma']=='Yes'])*100/len(data_male),\n",
    "                            len(data_male.loc[(data_male['asthma']=='Yes') & (data_male['ahi']>5)])*100/len(data_male),\n",
    "                            len(data_male.loc[data_male['gerd']=='Yes'])*100/len(data_male),\n",
    "                            len(data_male.loc[(data_male['gerd']=='Yes')& (data_male['ahi']>5)])*100/len(data_male),\n",
    "                            len(data_male.loc[(data_male['gerd']=='Yes')& (data_male['asthma']=='Yes') & (data_male['allergies']=='Yes')])*100/len(data_male), \n",
    "                            len(data_male.loc[(data_male['gerd']=='Yes')& (data_male['asthma']=='Yes') & (data_male['allergies']=='Yes') & (data_male['ahi']>5)])*100/len(data_male)], \n",
    "                  }\n",
    "init_stats_df = pd.DataFrame(init_stats_dict, index = stats_labels )\n",
    "\n",
    "init_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Jt+aZEICIX37ENi0MSF9epOQSz0P27WTjs8F07pFc2bPmG63ngMMHzqEmdOn5VtPUZad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\nPvr0GAePRLD/4Cc8/sQTdO3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aODk5EW22rzORkfh41bHV5OtDZHSMxbzIqCj8fH3o0f1V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c8XcvbCBe2z18ebnrpnz71eI2ZO+5rOHdLd53Pmpd3nkyZQo7rhL+Q/r13n88HD8P3tapb18aZp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195a6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze colinearity of features and plot heatmap of corresponding values\n",
    "cols = ['gender','ethnicity','term', 'bmi','age','allergies','asthma','gerd','tonsilsize', 'ahi']\n",
    "cm = np.corrcoef(obs[cols].values.T)\n",
    "hm = sns.heatmap(cm, cbar = True, annot = True, fmt = '.2f', annot_kws={'size':10}, yticklabels = cols, xticklabels = cols)\n",
    "plt.title('Colinearity matrix of variables')\n",
    "plt.savefig('colinearity_matrix.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables Causality\n",
    "<img src=\"files/Variables_causality.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f472e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac6ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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spmRwsHNXiQ4MdGwoqacNDs5haKj3r9Bu/Hzo5M+JXmQdJlmLol1ff9tDQ0R8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abe2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119af5ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bHJzZ7RI2O45572rnYYvXAl8B5jUtjNwFWAbcCJwWEQOZ2Th8sU+9XdIUsHbt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\n/+TY74j4bgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac63668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variables histogram\n",
    "plt.hist(init_obs['ahi'], 50, range = (0, 50))\n",
    "plt.title('AHI Histogram')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(init_obs['age'], 16)\n",
    "plt.title('Age histogram')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(init_obs['bmi'])\n",
    "plt.title('BMI histogram')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(init_obs['gender'], 2)\n",
    "plt.title('Gender histogram')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(init_obs['term'],2)\n",
    "plt.title('Term histogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f6w8kx461VjW7sHC7zeeqHjr9HOluDxZ3A7YoCtEaN2Fz+cPJ8HDrFIEKolwu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\nl/37W2IPvAV58MF7+cEPHgRA64zSfvV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jRlTUBa6U0doiaMx/dYbue7y+vmOBDxsA1157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\nQv4DjQSrzPyb8877L0aNUt3dXNFOZ5+9mO3bP8ZtGBwKBhiSkcluvw8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f89358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigation of tonsil size and ahi \n",
    "tons = sns.violinplot(x='tonsilsize', y='ahi', data=data.loc[data['ahi']<20], inner = 'quartile')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Gaussian Bayesian Model\n",
    "This model assumes the likelihood of the features is Gaussian (for continuous variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.70 (+/- 0.21)\n",
      "AUROC score: 0.77 (+/- 0.17)\n"
     ]
    }
   ],
   "source": [
    "# Run on variables with continous distributions \n",
    "# Consider our problem as a binary one - as suggested by Amal, to diagnose \n",
    "# children with 0 (no apnea<5) or 1 (apnea>5)\n",
    "\n",
    "# (1) Parse observations to change ahi values accordingly \n",
    "data = init_obs\n",
    "# add an extra column to indicate the boolean value of ahi \n",
    "data['bool_ahi'] = data['ahi'].between(5,188)\n",
    "\n",
    "# (2) Choose continuous independent features according to previous colinearity analysis\n",
    "cols = ['bmi','tonsilsize', 'term','gender']\n",
    "        #'gender', 'ethnicity']\n",
    "    \n",
    "# (3) Run Gaussian Bayesian assuming Gaussian distributions for variables, to give parameters of model\n",
    "clf = GaussianNB() \n",
    "acc_scores = cross_val_score(clf, data[list(cols)].values, data['bool_ahi'].values, cv=10, scoring ='accuracy')\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (acc_scores.mean(), acc_scores.std() * 2))\n",
    "\n",
    "#f1_scores = cross_val_score(clf, data[list(cols)].values, data['bool_ahi'].values, cv=10, scoring ='f1')\n",
    "#print(\"F1_score: %0.2f (+/- %0.2f)\" % (f1_scores.mean(), f1_scores.std() * 2))\n",
    "\n",
    "auroc_scores = cross_val_score(clf, data[list(cols)].values, data['bool_ahi'].values, cv=10, scoring ='roc_auc')\n",
    "print(\"AUROC score: %0.2f (+/- %0.2f)\" % (auroc_scores.mean(), auroc_scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Multinomial Bayesian Distribution\n",
    "Classification with discrete features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.59 (+/- 0.02)\n",
      "AUROC score: 0.68 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "# (1) Convert bmi to a discrete variable (add to bin)\n",
    "def round_10(x):\n",
    "       return int(math.ceil(x / 10.0)) \n",
    "\n",
    "disc_obs = obs\n",
    "disc_obs['bmi'] = disc_obs['bmi'].apply(round_10)\n",
    "\n",
    "# add an extra column to indicate the boolean value of ahi \n",
    "disc_obs['bool_ahi'] = disc_obs['ahi'].between(5,188)\n",
    "\n",
    "# (2) Choose continuous independent features according to previous colinearity analysis\n",
    "cols = ['bmi','tonsilsize', 'term','gender']\n",
    "        #'gender', 'ethnicity']\n",
    "    \n",
    "# (3) Run Multinomial Bayesian assuming for classification with discrete features\n",
    "clf = MultinomialNB() \n",
    "acc_scores = cross_val_score(clf, disc_obs[list(cols)].values, disc_obs['bool_ahi'].values, cv=10, scoring ='accuracy')\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (acc_scores.mean(), acc_scores.std() * 2))\n",
    "\n",
    "#f1_scores = cross_val_score(clf, disc_obs[list(cols)].values, disc_obs['bool_ahi'].values, cv=10, scoring ='f1')\n",
    "#print(\"F1_score: %0.2f (+/- %0.2f)\" % (f1_scores.mean(), f1_scores.std() * 2))\n",
    "\n",
    "auroc_scores = cross_val_score(clf, disc_obs[list(cols)].values, disc_obs['bool_ahi'].values, cv=10, scoring ='roc_auc')\n",
    "print(\"AUROC score: %0.2f (+/- %0.2f)\" % (auroc_scores.mean(), auroc_scores.std() * 2))"
   ]
  }
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