Semana 5 - COVID-19 testing/Semana 5 - COVID decision tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Model for COVID-19 testing\n",
    "Dataset available at: https://raw.githubusercontent.com/diogocortiz/Crash-Course-IA/master/ArvoreDecis%C3%A3o/dataset_einstein.csv\n",
    "\n",
    "This dataset contains data on patients who tested for COVID-18 on Albert Einstein Hospital, in São Paulo, Brazil. Has features such as \n",
    "leukocytes, protein, creatinine and others. \n",
    "\n",
    "Question to be answered: is it to possible to build a prediction modelthat can tell, based on the dataset attributes, if the person will test positive or negative to COVID-19?\n",
    "\n",
    "**This project was designed merely for learning purposes and based on the AI Crash Course by Diogo Cortis, available on YouTube: https://www.youtube.com/watch?v=ecYpXd4WREk. It's use for medical purpose is forbidden and imprecise.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Patient ID</th>\n",
       "      <th>Patient age quantile</th>\n",
       "      <th>SARS-Cov-2 exam result</th>\n",
       "      <th>Patient addmited to regular ward (1=yes. 0=no)</th>\n",
       "      <th>Patient addmited to semi-intensive unit (1=yes. 0=no)</th>\n",
       "      <th>Patient addmited to intensive care unit (1=yes. 0=no)</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>Leukocytes</th>\n",
       "      <th>Basophils</th>\n",
       "      <th>Proteina C reativa mg/dL</th>\n",
       "      <th>Creatinine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44477f75e8169d2</td>\n",
       "      <td>13</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126e9dd13932f68</td>\n",
       "      <td>17</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.02234</td>\n",
       "      <td>-0.09461</td>\n",
       "      <td>-0.223767</td>\n",
       "      <td>-0.147895</td>\n",
       "      <td>2.089928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a46b4402a0e5696</td>\n",
       "      <td>8</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f7d619a94f97c45</td>\n",
       "      <td>5</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d9e41465789c2b5</td>\n",
       "      <td>15</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Patient ID  Patient age quantile SARS-Cov-2 exam result  \\\n",
       "0  44477f75e8169d2                    13               negative   \n",
       "1  126e9dd13932f68                    17               negative   \n",
       "2  a46b4402a0e5696                     8               negative   \n",
       "3  f7d619a94f97c45                     5               negative   \n",
       "4  d9e41465789c2b5                    15               negative   \n",
       "\n",
       "   Patient addmited to regular ward (1=yes. 0=no)  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   Patient addmited to semi-intensive unit (1=yes. 0=no)  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "3                                                  0       \n",
       "4                                                  0       \n",
       "\n",
       "   Patient addmited to intensive care unit (1=yes. 0=no)  Hemoglobin  \\\n",
       "0                                                  0             NaN   \n",
       "1                                                  0        -0.02234   \n",
       "2                                                  0             NaN   \n",
       "3                                                  0             NaN   \n",
       "4                                                  0             NaN   \n",
       "\n",
       "   Leukocytes  Basophils  Proteina C reativa mg/dL  Creatinine  \n",
       "0         NaN        NaN                       NaN         NaN  \n",
       "1    -0.09461  -0.223767                 -0.147895    2.089928  \n",
       "2         NaN        NaN                       NaN         NaN  \n",
       "3         NaN        NaN                       NaN         NaN  \n",
       "4         NaN        NaN                       NaN         NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attributing the dataset to a dataframe and specifying 'delimiter' so it can read properly\n",
    "dataset = pd.read_csv('https://raw.githubusercontent.com/diogocortiz/Crash-Course-IA/master/ArvoreDecis%C3%A3o/dataset_einstein.csv', \n",
    "                      delimiter=';')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5644, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative    5086\n",
      "positive     558\n",
      "Name: SARS-Cov-2 exam result, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking the amount of negative x positive results for covid testing\n",
    "print(dataset['SARS-Cov-2 exam result'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Creatinine                  5220\n",
       "Proteina C reativa mg/dL    5138\n",
       "Basophils                   5042\n",
       "Leukocytes                  5042\n",
       "Hemoglobin                  5041\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing data\n",
    "dataset.isnull().sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# building a heatmap for corrrlation\n",
    "# annot = True gives the numbers on the corr matrix\n",
    "# linewidths=0.2 make the lines larger for better visualization\n",
    "# cmap='coolwarm' facilitates visualization through collors\n",
    "# square = True asserts the legend into the heatmap properly\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(data=dataset.corr(),annot=True,linewidths=0.2,cmap='coolwarm', square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Acccording to the correlation matrix, the level of Leukocyts is one of the features that are highly correlated to COVID testing. \n",
    "- Along with that, 'patient addmited to regular ward' is the second on the line of correlation. \n",
    "- We shouldn't use this variable in our training model tho: since it's a pandemia, the majority of the patinets addmited will usually be related to testing for the disease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing data\n",
    "There are a lot of rows that don't have all of the data filled out. Since is a health matter, It doesn't seem smart to assign mean or median. Therefore, I've decided to drop those rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping missing data\n",
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(357, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative    302\n",
      "positive     55\n",
      "Name: SARS-Cov-2 exam result, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking if we have a ballanced amount of negative and positive results\n",
    "print(dataset['SARS-Cov-2 exam result'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Data cleaning\n",
    "- model can only handle features that are numeric\n",
    "- need to convert this dataset into an Array Numpy for the algorithm to work\n",
    "- split the dataset in two: target variable and indepent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming the target variable in binomial 0 - 1\n",
    "dataset['SARS-Cov-2 exam result'] = [0 if x == 'negative' else 1 for x in dataset['SARS-Cov-2 exam result']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Patient ID</th>\n",
       "      <th>Patient age quantile</th>\n",
       "      <th>SARS-Cov-2 exam result</th>\n",
       "      <th>Patient addmited to regular ward (1=yes. 0=no)</th>\n",
       "      <th>Patient addmited to semi-intensive unit (1=yes. 0=no)</th>\n",
       "      <th>Patient addmited to intensive care unit (1=yes. 0=no)</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>Leukocytes</th>\n",
       "      <th>Basophils</th>\n",
       "      <th>Proteina C reativa mg/dL</th>\n",
       "      <th>Creatinine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126e9dd13932f68</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.022340</td>\n",
       "      <td>-0.094610</td>\n",
       "      <td>-0.223767</td>\n",
       "      <td>-0.147895</td>\n",
       "      <td>2.089928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8bb9d64f0215244</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.774212</td>\n",
       "      <td>0.364550</td>\n",
       "      <td>-0.223767</td>\n",
       "      <td>-0.286986</td>\n",
       "      <td>-1.838623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fc41531ca4faf1e</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.854844</td>\n",
       "      <td>-0.075131</td>\n",
       "      <td>2.525365</td>\n",
       "      <td>-0.434025</td>\n",
       "      <td>-0.701411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>891d0f6449ff3d7</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.854844</td>\n",
       "      <td>0.105751</td>\n",
       "      <td>-0.529226</td>\n",
       "      <td>-0.529401</td>\n",
       "      <td>0.332418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ebdd7c67fcb21b4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.711556</td>\n",
       "      <td>-0.286623</td>\n",
       "      <td>-0.223767</td>\n",
       "      <td>0.545572</td>\n",
       "      <td>1.021638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Patient ID  Patient age quantile  SARS-Cov-2 exam result  \\\n",
       "1   126e9dd13932f68                    17                       0   \n",
       "8   8bb9d64f0215244                     1                       0   \n",
       "28  fc41531ca4faf1e                    13                       0   \n",
       "29  891d0f6449ff3d7                    14                       0   \n",
       "30  ebdd7c67fcb21b4                     9                       0   \n",
       "\n",
       "    Patient addmited to regular ward (1=yes. 0=no)  \\\n",
       "1                                                0   \n",
       "8                                                0   \n",
       "28                                               0   \n",
       "29                                               0   \n",
       "30                                               1   \n",
       "\n",
       "    Patient addmited to semi-intensive unit (1=yes. 0=no)  \\\n",
       "1                                                   0       \n",
       "8                                                   1       \n",
       "28                                                  0       \n",
       "29                                                  0       \n",
       "30                                                  0       \n",
       "\n",
       "    Patient addmited to intensive care unit (1=yes. 0=no)  Hemoglobin  \\\n",
       "1                                                   0       -0.022340   \n",
       "8                                                   0       -0.774212   \n",
       "28                                                  0        0.854844   \n",
       "29                                                  0        0.854844   \n",
       "30                                                  0       -0.711556   \n",
       "\n",
       "    Leukocytes  Basophils  Proteina C reativa mg/dL  Creatinine  \n",
       "1    -0.094610  -0.223767                 -0.147895    2.089928  \n",
       "8     0.364550  -0.223767                 -0.286986   -1.838623  \n",
       "28   -0.075131   2.525365                 -0.434025   -0.701411  \n",
       "29    0.105751  -0.529226                 -0.529401    0.332418  \n",
       "30   -0.286623  -0.223767                  0.545572    1.021638  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1       0\n",
      "8       0\n",
      "28      0\n",
      "29      0\n",
      "30      0\n",
      "       ..\n",
      "5602    0\n",
      "5614    0\n",
      "5615    0\n",
      "5618    0\n",
      "5643    1\n",
      "Name: SARS-Cov-2 exam result, Length: 357, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# creating the target variable dataset (y)\n",
    "y = dataset['SARS-Cov-2 exam result']\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.02234027 -0.09461035 -0.22376651 -0.14789495  2.08992839]\n",
      " [-0.774212    0.36455047 -0.22376651 -0.28698576 -1.83862317]\n",
      " [ 0.85484386 -0.07513076  2.52536511 -0.4340246  -0.70141089]\n",
      " ...\n",
      " [ 1.10546756 -0.5509882  -0.22376651  0.5614683  -0.49464503]\n",
      " [-2.77920342 -1.73367476 -1.14014375  0.60915661  0.4702622 ]\n",
      " [ 0.54156393 -1.28842807 -1.14014375 -0.50357002 -0.73587185]]\n"
     ]
    }
   ],
   "source": [
    "# creating the independent variable dataset (X)\n",
    "X = dataset[['Hemoglobin', 'Leukocytes', 'Basophils', 'Proteina C reativa mg/dL', 'Creatinine']].values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting data to build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libs\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting data\n",
    "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building algorith using decision tree\n",
    "# criteria set for this tree: entropy\n",
    "decision_tree = DecisionTreeClassifier(criterion='entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training the algorithm\n",
    "model = decision_tree.fit(X_treino, y_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying the model on TEST\n",
    "prediction = model.predict(X_teste)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evalutating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8194444444444444\n"
     ]
    }
   ],
   "source": [
    "# accuracy of the tree\n",
    "tree_accuracy = accuracy_score(y_teste, prediction)\n",
    "print(tree_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.93      0.90        60\n",
      "           1       0.43      0.25      0.32        12\n",
      "\n",
      "    accuracy                           0.82        72\n",
      "   macro avg       0.65      0.59      0.61        72\n",
      "weighted avg       0.79      0.82      0.80        72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ploting confusion matrix to see if the lack of positive testing has impacted the model\n",
    "confusion_matrix = (classification_report(y_teste, prediction))\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "- Analysing the confusion matrix, we can see that the model has a problem when it comes to predicting positive results: he is right in 25% of the times. \n",
    "- This is due the short amount of patients who tested positive when compared to the amount who tested negative (302 neagtives against 55 positives). \n",
    "- Although our accuracy was around 82%, we would need a bigger dataset with more patients who tested positive in order to provide some sort of reliable prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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