{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e519b28-7afa-4786-8d9e-411785ce45e5",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "# Heart Failure Prediction\n",
    "**Project by Group 21: Hannah Cooper, Joselin Hartanto, Nancy Xu, James Xu**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628a72b0-9236-4116-bca7-9f6263009139",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/UCB-stat-159-s23/project-Group21.git/HEAD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33c3605-2fa8-482a-83e1-93a9f9e71dcb",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "## Purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd1d799-8a34-473e-8863-0cc0a4027c1e",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The purpose of this project is to analyze the prediction of Heart failure. Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.\n",
    "\n",
    "People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5fe075-8f50-4679-95c5-4bb3a7106ab5",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f1704b4-3193-434c-b01d-21d7a26ec3fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea8d32e-361e-4a9f-9190-fb98633e27d7",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "#### Data from https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa335258-e99f-4fcd-97a7-1a323fcb6781",
   "metadata": {},
   "source": [
    "This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:\n",
    "\n",
    "- Cleveland: 303 observations\n",
    "- Hungarian: 294 observations\n",
    "- Switzerland: 123 observations\n",
    "- Long Beach VA: 200 observations\n",
    "- Stalog (Heart) Data Set: 270 observations\n",
    "\n",
    "\n",
    "Total: 1190 observations, \n",
    "Duplicated: 272 observations,\n",
    "Final dataset: 918 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c052f3d9-255a-4d8c-b0cc-d1f0c5a4f764",
   "metadata": {
    "tags": []
   },
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import raw data\n",
    "raw_data = pd.read_csv('data/heart.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b4582f3-2f5a-4259-9417-78a77782a3ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age               0\n",
      "Sex               0\n",
      "ChestPainType     0\n",
      "RestingBP         0\n",
      "Cholesterol       0\n",
      "FastingBS         0\n",
      "RestingECG        0\n",
      "MaxHR             0\n",
      "ExerciseAngina    0\n",
      "Oldpeak           0\n",
      "ST_Slope          0\n",
      "HeartDisease      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nan_count = raw_data.isna().sum()\n",
    "print(nan_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a7ad42-e3f9-4bc8-807e-6b368601e94e",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "We note that the categorical columns in the data are Sex, ChestPainType, RestingECG, ExerciseAngina, and ST_Slope, while the rest of the features are quantitative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac853859-8509-45f0-b3fb-70d4f1d8ec65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data.hist(figsize=(14, 10))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1ac841-9a6c-48f7-8949-df41a4769b2b",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "Observing the overall distributions for the quantitative features, we see that most of the distributions are skewed. We also observe a noticeable class imbalance in the label, HeartDisease, which may lead to difficulties when we try to fit models to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df557a45-9262-428d-a4e7-2de4125c6a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=3,ncols=5,figsize=(16,10))\n",
    "\n",
    "# loop through each column in the dataframe\n",
    "for i, col in enumerate(raw_data.columns[:-1]):\n",
    "    # select the appropriate subplot axis based on the index i\n",
    "    ax = axes[i // 5, i % 5]\n",
    "    # plot a histogram of the column for each group in 'HeartDisease'\n",
    "    raw_data.groupby('HeartDisease')[col].hist(alpha=0.4, ax=ax)\n",
    "    # set the title, y-axis label, and x-axis label for the subplot\n",
    "    ax.set_title(col)\n",
    "    ax.set_ylabel(\"Count\")\n",
    "    ax.set_xlabel(\"Value\")\n",
    "    ax.legend(raw_data['HeartDisease'])\n",
    "\n",
    "fig.delaxes(axes[2][1])\n",
    "fig.delaxes(axes[2][2])\n",
    "fig.delaxes(axes[2][3])\n",
    "fig.delaxes(axes[2][4])\n",
    "# adjust the spacing between the subplots for better readability\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8117418-1397-4488-a5b8-4f6946504c5e",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "Observing the class distribution, we observe that the features Age, Sex, MaxHR, ExerciseAngina, ST_Slopem and OldPeak seem to have noticeably different distribtuions, which indicate that there is a significant variance in the data. We can perform PCA on the non categorical features, to take a look at the structure of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "004f11fb-3501-4374-928b-5cebb48528c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove categorical features\n",
    "categorical_columns= ['Sex', 'ChestPainType','RestingECG', 'ExerciseAngina', 'ST_Slope' ]\n",
    "X_pca = raw_data.drop(columns=categorical_columns)\n",
    "y_pca = raw_data['HeartDisease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12adf9f0-31a4-4a2e-88c4-22334b150965",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the features since they are of different units\n",
    "scaler = StandardScaler()\n",
    "X_standardized = scaler.fit_transform(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac7e8c4e-574d-4aed-8153-b3eeded19d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using PCA\n",
    "pca = PCA(n_components = 7);\n",
    "pca.fit(X_standardized);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "514a6968-95af-4bfa-ae20-f15b2c554963",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.417053</td>\n",
       "      <td>-0.508044</td>\n",
       "      <td>-0.536672</td>\n",
       "      <td>0.492483</td>\n",
       "      <td>-0.592140</td>\n",
       "      <td>0.263087</td>\n",
       "      <td>0.035454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.138073</td>\n",
       "      <td>-0.813523</td>\n",
       "      <td>-0.245526</td>\n",
       "      <td>0.495047</td>\n",
       "      <td>-1.577625</td>\n",
       "      <td>-0.012082</td>\n",
       "      <td>0.836385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.346575</td>\n",
       "      <td>0.120716</td>\n",
       "      <td>0.050249</td>\n",
       "      <td>-1.194085</td>\n",
       "      <td>-0.405928</td>\n",
       "      <td>1.707199</td>\n",
       "      <td>-1.379748</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.768445</td>\n",
       "      <td>-0.416296</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>-0.468257</td>\n",
       "      <td>-0.780828</td>\n",
       "      <td>0.872467</td>\n",
       "      <td>-0.231420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.519631</td>\n",
       "      <td>-0.324544</td>\n",
       "      <td>-1.160150</td>\n",
       "      <td>-1.158588</td>\n",
       "      <td>-0.430199</td>\n",
       "      <td>0.032315</td>\n",
       "      <td>-0.483574</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0 -2.417053 -0.508044 -0.536672  0.492483 -0.592140  0.263087  0.035454   \n",
       "1  0.138073 -0.813523 -0.245526  0.495047 -1.577625 -0.012082  0.836385   \n",
       "2 -1.346575  0.120716  0.050249 -1.194085 -0.405928  1.707199 -1.379748   \n",
       "3  0.768445 -0.416296  0.876559 -0.468257 -0.780828  0.872467 -0.231420   \n",
       "4 -0.519631 -0.324544 -1.160150 -1.158588 -0.430199  0.032315 -0.483574   \n",
       "\n",
       "   HeartDisease  \n",
       "0             0  \n",
       "1             1  \n",
       "2             0  \n",
       "3             1  \n",
       "4             0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining the principal components\n",
    "p_c = pca.fit_transform(X_standardized)\n",
    "# print(principalComponents)\n",
    "principal_df = pd.DataFrame(data=p_c, columns = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7'])\n",
    "final_df = pd.concat([principal_df, y_pca],axis=1)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "744f248f-0c26-457e-8d18-55c65d3ef4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 Component PCA of Non Categorical Data', fontsize = 20)\n",
    "targets = [0,1]\n",
    "colors = ['r', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = final_df['HeartDisease'] == target\n",
    "    ax.scatter(final_df.loc[indicesToKeep, 'PC1']\n",
    "               , final_df.loc[indicesToKeep, 'PC2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4691d78f-4a25-4a1c-98eb-0af6e831c449",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "We observe that there seem to be two somewhat distinct clusters, when we project only the non categorical data onto the first two principal components. This indicates that there is indeed seperability of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "981b873c-6c07-4ca9-8e19-9f93bfa4e93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Variability of components\n",
    "cum_var = pca.explained_variance_ratio_.cumsum()\n",
    "cum_var=np.insert(cum_var,0,0)\n",
    "k_features = list(range(0,X_pca.shape[1]+1))\n",
    "\n",
    "# Plot figure\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.plot(k_features,cum_var)\n",
    "# plt.rc('axes', titlesize=14)\n",
    "# plt.rc('axes', labelsize=12)\n",
    "ax.set_xlabel(\"Number of Eigenvalues\")\n",
    "ax.set_ylabel(\"Cumulative Sum of Variance\")\n",
    "ax.set_title(\"Cumulative Sum of Variance vs Number of Eigenvalues\")\n",
    "ax.set_xlim([0,7])\n",
    "ax.set_ylim([0,1.01])\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5774b248-2944-452c-a5aa-80a631cb910e",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "We note that the cumulative sum of variance is distributed relatively evenly among the principal components. We note that 5 principal components explains around 85% of the variability in the non categorical data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e38fe01-4eb2-44c6-af55-88ddbdb0058b",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3546188-2f6d-492d-a7f8-2c74549c3c61",
   "metadata": {},
   "source": [
    "In this section, we will preprocess the data and apply various models to predict whether someone has heart disease based on the attributes in the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffdcd5a-51ad-4513-b24e-9c4c5764f837",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d0e4320-d247-46c7-b758-47c07914f795",
   "metadata": {},
   "outputs": [],
   "source": [
    "from analysis import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c74612-ea15-45ad-b9f4-91b012feaffb",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We import util functions from util.py to clean the data. This  function one hot encodes the categorical columns, and uses sklearn's preprocessing function MinMaxScaler() to scale the data. It returns the X dataframe with all the features, and and labels y. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88631c97-8ac7-4972-9ad0-22bf39606c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "X,y = utils.clean_data(raw_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6409153c-4169-4d2b-a20b-3a399971ef30",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We inspect the columns/features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f64b0cd3-9a1f-4c9c-8061-e488625500bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Cholesterol',\n",
       " 'FastingBS',\n",
       " 'MaxHR',\n",
       " 'Oldpeak',\n",
       " 'RestingBP',\n",
       " 'Sex_F',\n",
       " 'Sex_M',\n",
       " 'ChestPainType_ASY',\n",
       " 'ChestPainType_ATA',\n",
       " 'ChestPainType_NAP',\n",
       " 'ChestPainType_TA',\n",
       " 'RestingECG_LVH',\n",
       " 'RestingECG_Normal',\n",
       " 'RestingECG_ST',\n",
       " 'ExerciseAngina_N',\n",
       " 'ExerciseAngina_Y',\n",
       " 'ST_Slope_Down',\n",
       " 'ST_Slope_Flat',\n",
       " 'ST_Slope_Up']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c4b7f4-2c11-4a0a-803f-19f3417f8e3c",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We then split the data into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03d41770-3a8c-4405-81f8-9f7a426c1a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72491f3-12a8-4a2f-bf1c-e90e7ffff0f0",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc400893-aeae-4ecf-9ed4-2b8ab7ff279e",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "We fit logistic regression, mlp, and random forest models using x_train and y_train, then choose the best model based on the model test accuracies. We then plot the roc curve and saves the roc cure to the folder ./figures. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffba968-3f56-4877-8307-c27838843089",
   "metadata": {},
   "source": [
    "The main question we want to focus on is: can models prioritize recall over precision? In the case of predicting heart disease, we care more about FN than FP, since we want to identify as many actual cases of heart disease as possible. This preference would determine how we create the threshold for classification, which we will discuss later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbbcba31-deb8-4a82-abc7-318db5f8fbd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "logisticRegr, clf, RF = utils.fit_models(x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9aa581e8-e3e2-4422-a5cc-a365b8494115",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model, best_test_predictions, best_model_name = utils.choose_best_model(logisticRegr, clf, RF, x_test, y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cda20a95-de7f-43ed-bf54-15a26634aa1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model is: logistic regression\n",
      "Recall is: 0.8439716312056738\n",
      "Precision is: 0.937007874015748\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=1.000000\n",
      "Best model AUC is: 0.8617078205030198\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.graph_confusion_roc(best_model, x_test, y_test, best_test_predictions, best_model_name, root_dir = '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e359e832-23e6-46b3-b312-979698ac6e36",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We see that there are more FP than FN, and that although they are pretty close, recall is less than the precision. In our case of predicting heart disease, we probably want to prioritize recall, since we want to detect as many positive cases of heart disease as possible. In other words, we care more about the FN than the FP. Thus we would want to set the threshold to be lower than 0.5 to maximize the recall. Additionally, we could also try to inspect the data that are FN to try to identify a pattern, and then we could augment the training set with these FN's so that the model will learn to better identify positive cases.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a3add4-2e9d-4fa6-9035-e1f6f42a3deb",
   "metadata": {},
   "source": [
    "# Author Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406844e6-f1f9-45f2-965b-b7c716582332",
   "metadata": {},
   "source": [
    "Hannah Cooper created the tests for the data preprocessing modeling functions, the license for the project, and created the makefile. Joseline Hartanto created the environment yml file, jupyter book, binder, and contributed to main.ipynb file. James Xu created the notebook for EDA + data cleaning + processing with the corresponding functions, contributed to main.ipynb file, and helped with reproducibility support. Nancy Xu created the notebook for modeling, contributed to main.ipynb file, and helped with testing functions."
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