analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the presence of heart disease with key health metrics and attributes\n",
    "\n",
    "by Ethan Fang, Caroline Kahare and Alex Wong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction \n",
    "\n",
    "The objective of this project is to build a classification model that predicts the presence of heart disease based on key health metrics and attributes. It aims to contribute to the understanding and early detection of heart disease, which is crucial for effective medical intervention and prevention. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data \n",
    "\n",
    "The dataset is created by R. Detrano, A. Jánosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, V. Froelicher and was sourced from UC Irvine's Machine Learning Repository. The original dataset consists of 920 observation and 76 attributes, however only 13 attributes were used for the project. The target variable, label, indicates the presence or absence of heart disease, with values ranging from 0 (no presence) to 4 (indicating varying levels of severity). The dataset also contains demographic, clinical, and diagnostic attributes, offering a comprehensive view of patient health metrics.\n",
    "\n",
    "Key features of the dataset include demographic indicators such as age (age in years) and sex (gender: 1 = male, 0 = female). Clinical measurements include trestbps (resting blood pressure), chol (serum cholesterol levels), thalach (maximum heart rate achieved), and oldpeak (ST depression induced by exercise). Additionally, categorical variables such as cp (chest pain type), fbs (fasting blood sugar > 120 mg/dl), restecg (resting electrocardiographic results), exang (exercise-induced angina), slope (slope of the peak exercise ST segment), ca (number of major vessels colored by fluoroscopy), and thal (heart imaging defects) provide valuable context for predicting heart disease. \n",
    "\n",
    "\n",
    "1. **age**: Age in years  \n",
    "2. **sex**: Sex  \n",
    "   - `1` = Male  \n",
    "   - `0` = Female  \n",
    "3. **cp**: Chest pain type  \n",
    "   - Value `1`: Typical angina  \n",
    "   - Value `2`: Atypical angina  \n",
    "   - Value `3`: Non-anginal pain  \n",
    "   - Value `4`: Asymptomatic  \n",
    "4. **trestbps**: Resting blood pressure (in mm Hg on admission to the hospital)  \n",
    "5. **chol**: Serum cholesterol in mg/dl  \n",
    "6. **fbs**: Fasting blood sugar (> 120 mg/dl)  \n",
    "   - `1` = True  \n",
    "   - `0` = False  \n",
    "7. **restecg**: Resting electrocardiographic results  \n",
    "   - Value `0`: Normal  \n",
    "   - Value `1`: Having ST-T wave abnormality (T wave inversions and/or ST  \n",
    "     elevation or depression of > 0.05 mV)  \n",
    "   - Value `2`: Showing probable or definite left ventricular hypertrophy by  \n",
    "     Estes' criteria  \n",
    "8. **thalach**: Maximum heart rate achieved  \n",
    "9. **exang**: Exercise-induced angina  \n",
    "   - `1` = Yes  \n",
    "   - `0` = No  \n",
    "10. **oldpeak**: ST depression induced by exercise relative to rest  \n",
    "11. **slope**: The slope of the peak exercise ST segment  \n",
    "    - Value `1`: Upsloping  \n",
    "    - Value `2`: Flat  \n",
    "    - Value `3`: Downsloping  \n",
    "12. **ca**: Number of major vessels (0-3) colored by fluoroscopy  \n",
    "13. **thal**:  \n",
    "    - `3` = Normal  \n",
    "    - `6` = Fixed defect  \n",
    "    - `7` = Reversible defect\n",
    "14. **label**: \n",
    "    - `0` = Absence \n",
    "    - `1` = Presence \n",
    "    - `2` = Presence\n",
    "    - `3` = Presence\n",
    "    - `4` = Presence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['age','sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hungary_df = pd.read_csv('data/processed.hungarian.data',index_col=False, names = columns)\n",
    "swiss_df = pd.read_csv('data/processed.switzerland.data',index_col=False, names = columns)\n",
    "cleveland_df = pd.read_csv('data/processed.cleveland.data',index_col=False, names = columns)\n",
    "va_df = pd.read_csv('data/processed.va.data',index_col=False, names = columns)\n",
    "\n",
    "# Combine the four dataset into one consolidated set \n",
    "combined_df = pd.concat([hungary_df, swiss_df, cleveland_df, va_df], axis = 0)\n",
    "\n",
    "# Replacing ? with NaN values \n",
    "combined_df.replace('?', np.nan, inplace = True)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 920 entries, 0 to 199\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       920 non-null    float64\n",
      " 1   sex       920 non-null    float64\n",
      " 2   cp        920 non-null    float64\n",
      " 3   trestbps  861 non-null    object \n",
      " 4   chol      890 non-null    object \n",
      " 5   fbs       830 non-null    object \n",
      " 6   restecg   918 non-null    object \n",
      " 7   thalach   865 non-null    object \n",
      " 8   exang     865 non-null    object \n",
      " 9   oldpeak   858 non-null    object \n",
      " 10  slope     611 non-null    object \n",
      " 11  ca        309 non-null    object \n",
      " 12  thal      434 non-null    object \n",
      " 13  label     920 non-null    int64  \n",
      "dtypes: float64(3), int64(1), object(10)\n",
      "memory usage: 107.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking datatypes of each feature\n",
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casting continuous features to float64 instead of categories \n",
    "combined_df['trestbps'] = combined_df['trestbps'].astype('float64')\n",
    "combined_df['chol'] = combined_df['chol'].astype('float64')\n",
    "combined_df['thalach'] = combined_df['thalach'].astype('float64')\n",
    "combined_df['oldpeak'] = combined_df['thalach'].astype('float64')\n",
    "\n",
    "# Casting label as categorical \n",
    "combined_df['label'] = combined_df['label'].astype('category')\n",
    "\n",
    "# Casting as categorical \n",
    "combined_df['cp'] = combined_df['cp'].astype('object')\n",
    "combined_df['sex'] = combined_df['sex'].astype('object')\n",
    "\n",
    "# For the following features, have to first convert dtype to number first to ensure the category labels \n",
    "# are not affected by decimals (i.e. 1.0 and 1 are not treated as different groups)\n",
    "combined_df['exang'] = pd.to_numeric(combined_df['exang'], errors='coerce').astype('object')\n",
    "combined_df['thal'] = pd.to_numeric(combined_df['thal'], errors='coerce').astype('object')\n",
    "combined_df['fbs'] = pd.to_numeric(combined_df['fbs'], errors='coerce').astype('object')\n",
    "combined_df['ca'] = pd.to_numeric(combined_df['ca'], errors='coerce').astype('object')\n",
    "combined_df['slope'] = pd.to_numeric(combined_df['slope'], errors='coerce').astype('object')\n",
    "combined_df['restecg'] = pd.to_numeric(combined_df['restecg'], errors='coerce').astype('object')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(combined_df, test_size=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs restecg  thalach exang  oldpeak  \\\n",
       "232  46.0  1.0  4.0     130.0  222.0  0.0     0.0    112.0   0.0    112.0   \n",
       "150  54.0  1.0  2.0     120.0  246.0  0.0     0.0    110.0   0.0    110.0   \n",
       "112  66.0  1.0  4.0     150.0    0.0  0.0     0.0    108.0   1.0    108.0   \n",
       "132  53.0  0.0  2.0     140.0  216.0  0.0     0.0    142.0   1.0    142.0   \n",
       "9    34.0  0.0  2.0     130.0  161.0  0.0     0.0    190.0   0.0    190.0   \n",
       "254  44.0  1.0  4.0     130.0  290.0  0.0     0.0    100.0   1.0    100.0   \n",
       "69   63.0  1.0  2.0       NaN  217.0  1.0     1.0      NaN   NaN      NaN   \n",
       "82   46.0  1.0  4.0     110.0  238.0  0.0     1.0    140.0   1.0    140.0   \n",
       "64   43.0  0.0  2.0     150.0  186.0  0.0     0.0    154.0   0.0    154.0   \n",
       "144  58.0  1.0  3.0     105.0  240.0  0.0     2.0    154.0   1.0    154.0   \n",
       "\n",
       "    slope   ca thal label  \n",
       "232   NaN  NaN  NaN     1  \n",
       "150   NaN  NaN  NaN     0  \n",
       "112   2.0  NaN  7.0     3  \n",
       "132   2.0  NaN  NaN     0  \n",
       "9     NaN  NaN  NaN     0  \n",
       "254   2.0  NaN  NaN     1  \n",
       "69    NaN  NaN  NaN     1  \n",
       "82    2.0  NaN  3.0     0  \n",
       "64    NaN  NaN  NaN     0  \n",
       "144   2.0  0.0  7.0     0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age          0.000000\n",
      "sex          0.000000\n",
      "cp           0.000000\n",
      "trestbps     6.521739\n",
      "chol         2.795031\n",
      "fbs          8.850932\n",
      "restecg      0.310559\n",
      "thalach      6.055901\n",
      "exang        6.055901\n",
      "oldpeak      6.055901\n",
      "slope       34.316770\n",
      "ca          65.062112\n",
      "thal        51.242236\n",
      "label        0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check the percentage of missing values for each feature. \n",
    "\n",
    "missing_percentage = (train_df.isnull().sum() / len(train_df)) * 100\n",
    "print(missing_percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis\n",
    "\n",
    "#### Key findings\n",
    "\n",
    "- Patients with no heart disease exhibits on average higher ST depression induced by exercise relative to rest, higher maximum heart rate and lower serum cholestorel. \n",
    "\n",
    "- Heart disease is more common among patients over 55. \n",
    "\n",
    "- Patients with heart disease are more likely to experience asymptomatic chest pains.\n",
    "\n",
    "- Males appear to be more susceptible to heart disease. \n",
    "\n",
    "- Patients without heart disease tend to have lower fasting blood sugar when compared to the positive group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"age\": 46.0, \"sex\": 1.0, \"cp\": 4.0, \"trestbps\": 130.0, \"chol\": 222.0, \"fbs\": 0.0, \"restecg\": 0.0, \"thalach\": 112.0, \"exang\": 0.0, \"oldpeak\": 112.0, \"slope\": null, \"ca\": null, \"thal\": null, \"label\": 1}, {\"age\": 54.0, \"sex\": 1.0, \"cp\": 2.0, \"trestbps\": 120.0, \"chol\": 246.0, \"fbs\": 0.0, \"restecg\": 0.0, \"thalach\": 110.0, \"exang\": 0.0, \"oldpeak\": 110.0, \"slope\": null, \"ca\": null, \"thal\": null, \"label\": 0}, {\"age\": 66.0, \"sex\": 1.0, \"cp\": 4.0, \"trestbps\": 150.0, \"chol\": 0.0, \"fbs\": 0.0, \"restecg\": 0.0, \"thalach\": 108.0, \"exang\": 1.0, \"oldpeak\": 108.0, \"slope\": 2.0, \"ca\": null, \"thal\": 7.0, \"label\": 3}, {\"age\": 53.0, \"sex\": 0.0, \"cp\": 2.0, \"trestbps\": 140.0, \"chol\": 216.0, \"fbs\": 0.0, \"restecg\": 0.0, \"thalach\": 142.0, \"exang\": 1.0, \"oldpeak\": 142.0, \"slope\": 2.0, 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       "</script>"
      ],
      "text/plain": [
       "alt.ConcatChart(...)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair_ally as aly\n",
    "import altair as alt\n",
    "\n",
    "aly.alt.data_transformers.enable('vegafusion')\n",
    "\n",
    "aly.dist(train_df, color='label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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\"paddingInner\": 0.06}], \"layout\": {\"padding\": 20, \"columns\": 3, \"bounds\": \"full\", \"align\": \"each\"}, \"background\": \"white\", \"legends\": [{\"fill\": \"color\", \"symbolType\": \"square\"}], \"config\": {\"style\": {\"cell\": {\"stroke\": null}}}, \"padding\": 5}, {\"mode\": \"vega\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.ConcatChart(...)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aly.dist(train_df, dtype = 'object', color = 'label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "\n",
    "X_train = train_df.drop(columns=[\"label\"])\n",
    "X_test = test_df.drop(columns=[\"label\"])\n",
    "y_train = train_df[\"label\"]\n",
    "y_test = test_df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak'] # standard scaling for numerical features\n",
    "categorical_features = ['cp', 'restecg'] # onehot encoding for categorical features with > 2 classes\n",
    "binary_features = ['sex', 'exang', 'fbs'] # simple imputing on the binary features\n",
    "drop_features = ['thal', 'ca', 'slope'] # dropping features with signifcant NaN values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_transformer_pipe = make_pipeline(SimpleImputer(strategy = 'median'), StandardScaler())\n",
    "categorical_transfomer_pipe = make_pipeline(SimpleImputer(strategy = 'most_frequent'), OneHotEncoder(drop = 'if_binary', sparse_output = False)) \n",
    "imputer = SimpleImputer(strategy = 'most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = make_column_transformer(\n",
    "    (numeric_transformer_pipe, numeric_features),\n",
    "    (categorical_transfomer_pipe, categorical_features),\n",
    "    (imputer, binary_features),\n",
    "    (\"drop\", drop_features)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transformed = preprocessor.fit_transform(X_train)\n",
    "X_test_transformed = preprocessor.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ( \n",
    "    numeric_features +\n",
    "    preprocessor.named_transformers_['pipeline-2'].get_feature_names_out().tolist() + \n",
    "    binary_features\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
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       "      <th>cp_1.0</th>\n",
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       "      <th>restecg_0.0</th>\n",
       "      <th>restecg_1.0</th>\n",
       "      <th>restecg_2.0</th>\n",
       "      <th>sex</th>\n",
       "      <th>exang</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.046673</td>\n",
       "      <td>-0.660108</td>\n",
       "      <td>0.402221</td>\n",
       "      <td>-1.12703</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.325714</td>\n",
       "      <td>0.961415</td>\n",
       "      <td>-1.890497</td>\n",
       "      <td>-1.205551</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.059914</td>\n",
       "      <td>0.420908</td>\n",
       "      <td>0.122622</td>\n",
       "      <td>0.129303</td>\n",
       "      <td>0.129303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.085061</td>\n",
       "      <td>-0.1196</td>\n",
       "      <td>-0.389978</td>\n",
       "      <td>2.013803</td>\n",
       "      <td>2.013803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>-0.592847</td>\n",
       "      <td>-0.1196</td>\n",
       "      <td>0.392901</td>\n",
       "      <td>0.835991</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>640</th>\n",
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       "      <td>0.011522</td>\n",
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       "    <tr>\n",
       "      <th>641</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>0.47302</td>\n",
       "      <td>2.042431</td>\n",
       "      <td>-1.890497</td>\n",
       "      <td>-1.323332</td>\n",
       "      <td>-1.323332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>-1.019194</td>\n",
       "      <td>-1.308717</td>\n",
       "      <td>-0.576378</td>\n",
       "      <td>1.424897</td>\n",
       "      <td>1.424897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>644 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  trestbps      chol   thalach   oldpeak cp_1.0 cp_2.0 cp_3.0  \\\n",
       "0   -0.806021   -0.1196  0.178542 -1.048509 -1.048509    0.0    0.0    0.0   \n",
       "1    0.046673 -0.660108  0.402221  -1.12703  -1.12703    0.0    1.0    0.0   \n",
       "2    1.325714  0.961415 -1.890497 -1.205551 -1.205551    0.0    0.0    0.0   \n",
       "3   -0.059914  0.420908  0.122622  0.129303  0.129303    0.0    1.0    0.0   \n",
       "4   -2.085061   -0.1196 -0.389978  2.013803  2.013803    0.0    1.0    0.0   \n",
       "..        ...       ...       ...       ...       ...    ...    ...    ...   \n",
       "639 -0.592847   -0.1196  0.392901  0.835991  0.835991    0.0    1.0    0.0   \n",
       "640 -0.379674  0.691162 -1.890497  0.011522  0.011522    0.0    0.0    0.0   \n",
       "641   0.79278   -0.1196 -1.890497 -2.422623 -2.422623    0.0    0.0    0.0   \n",
       "642   0.47302  2.042431 -1.890497 -1.323332 -1.323332    0.0    0.0    0.0   \n",
       "643 -1.019194 -1.308717 -0.576378  1.424897  1.424897    0.0    0.0    1.0   \n",
       "\n",
       "    cp_4.0 restecg_0.0 restecg_1.0 restecg_2.0  sex exang  fbs  \n",
       "0      1.0         1.0         0.0         0.0  1.0   0.0  0.0  \n",
       "1      0.0         1.0         0.0         0.0  1.0   0.0  0.0  \n",
       "2      1.0         1.0         0.0         0.0  1.0   1.0  0.0  \n",
       "3      0.0         1.0         0.0         0.0  0.0   1.0  0.0  \n",
       "4      0.0         1.0         0.0         0.0  0.0   0.0  0.0  \n",
       "..     ...         ...         ...         ...  ...   ...  ...  \n",
       "639    0.0         1.0         0.0         0.0  1.0   0.0  0.0  \n",
       "640    1.0         1.0         0.0         0.0  1.0   1.0  0.0  \n",
       "641    1.0         1.0         0.0         0.0  1.0   0.0  0.0  \n",
       "642    1.0         0.0         1.0         0.0  1.0   1.0  0.0  \n",
       "643    0.0         1.0         0.0         0.0  0.0   0.0  0.0  \n",
       "\n",
       "[644 rows x 15 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_transformed = pd.DataFrame(X_train_transformed, columns = col_names)\n",
    "X_test_transformed = pd.DataFrame(X_test_transformed, columns = col_names)\n",
    "X_train_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning models application and hyperparameters tuning\n",
    "#### Summary\n",
    "\n",
    "- Various machine learning models were tested and optimized using hyperparameter tuning to identify the best-performing model.\n",
    "- Randomized Search CV was used to perform hyperparameter optimization for each model. Key parameters tuned include:\n",
    "   - Logistic Regression: Regularization strength(C), Solver(liblinear, lbfgs).\n",
    "   - Decision Tree: Maximum depth, Minimum samples per split.\n",
    "   - SVM: Regularization strength(C), Kernel type(linear, rbf).\n",
    "   - KNN: Number of neighbours, Weight type(uniform, distance).\n",
    "\n",
    "- Results:\n",
    "\n",
    "    <img src=\"docs/Best_Models.png\" alt=\"Model Summary Table\" width=\"800\"/>\n",
    "\n",
    "#### Conclusion\n",
    "\n",
    "- The K-Nearest Neighbors (KNN) model emerged as the best-performing classifier based on accuracy and weighted F1-score. Despite moderate overall performance, it provided reasonable balance across classes compared to other models.This study demonstrates the potential of leveraging machine learning techniques for predicting heart disease but also highlights the challenges posed by multi-class classification and limited data quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pio.templates.default = \"plotly_white\"\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#Models for scikit learn\n",
    "\n",
    "#Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import make_scorer, roc_auc_score\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(random_state = 123, max_iter=1000),\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Support Vector Machine': SVC(random_state = 123, probability=True),\n",
    "    'K-Nearest Neighbors': KNeighborsClassifier()\n",
    "}\n",
    "\n",
    "param_distributions = {\n",
    "    'Logistic Regression': {\n",
    "        'classifier__C': stats.loguniform(1e-3, 1e3),\n",
    "        'classifier__solver': ['liblinear', 'lbfgs']\n",
    "    },\n",
    "    'Decision Tree': {\n",
    "        'classifier__max_depth': [3, 5, 10],\n",
    "        'classifier__min_samples_split': stats.randint(2, 20)\n",
    "    },\n",
    "    'Support Vector Machine': {\n",
    "        'classifier__C': stats.loguniform(1e-2, 1e2),\n",
    "        'classifier__kernel': ['linear', 'rbf']\n",
    "    },\n",
    "    'K-Nearest Neighbors': {\n",
    "        'classifier__n_neighbors': stats.randint(3, 20),\n",
    "        'classifier__weights': ['uniform', 'distance']\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning hyperparameters for Logistic Regression using RandomizedSearchCV...\n",
      "Best parameters for Logistic Regression: {'classifier__C': np.float64(0.1767016940294795), 'classifier__solver': 'liblinear'}\n",
      "----------------------------------------\n",
      "Tuning hyperparameters for Decision Tree using RandomizedSearchCV...\n",
      "Best parameters for Decision Tree: {'classifier__max_depth': 10, 'classifier__min_samples_split': 16}\n",
      "----------------------------------------\n",
      "Tuning hyperparameters for Support Vector Machine using RandomizedSearchCV...\n",
      "Best parameters for Support Vector Machine: {'classifier__C': np.float64(0.31489116479568624), 'classifier__kernel': 'linear'}\n",
      "----------------------------------------\n",
      "Tuning hyperparameters for K-Nearest Neighbors using RandomizedSearchCV...\n",
      "Best parameters for K-Nearest Neighbors: {'classifier__n_neighbors': 9, 'classifier__weights': 'distance'}\n",
      "----------------------------------------\n",
      "Evaluating Logistic Regression on test set...\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.89      0.79       119\n",
      "           1       0.43      0.59      0.50        81\n",
      "           2       0.11      0.03      0.04        37\n",
      "           3       0.14      0.03      0.05        33\n",
      "           4       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.57       276\n",
      "   macro avg       0.28      0.31      0.28       276\n",
      "weighted avg       0.47      0.57      0.50       276\n",
      "\n",
      "Confusion Matrix:\n",
      "[[106  11   2   0   0]\n",
      " [ 26  48   3   4   0]\n",
      " [ 10  24   1   2   0]\n",
      " [  5  24   3   1   0]\n",
      " [  2   4   0   0   0]]\n",
      "----------------------------------------\n",
      "Evaluating Decision Tree on test set...\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.73      0.76       119\n",
      "           1       0.41      0.42      0.42        81\n",
      "           2       0.24      0.27      0.25        37\n",
      "           3       0.21      0.24      0.22        33\n",
      "           4       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.50       276\n",
      "   macro avg       0.33      0.33      0.33       276\n",
      "weighted avg       0.52      0.50      0.51       276\n",
      "\n",
      "Confusion Matrix:\n",
      "[[87 21  6  4  1]\n",
      " [15 34 15 17  0]\n",
      " [ 5 13 10  8  1]\n",
      " [ 3 13  8  8  1]\n",
      " [ 0  1  3  2  0]]\n",
      "----------------------------------------\n",
      "Evaluating Support Vector Machine on test set...\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.74      0.75       119\n",
      "           1       0.40      0.79      0.53        81\n",
      "           2       0.00      0.00      0.00        37\n",
      "           3       0.00      0.00      0.00        33\n",
      "           4       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.55       276\n",
      "   macro avg       0.23      0.31      0.26       276\n",
      "weighted avg       0.44      0.55      0.48       276\n",
      "\n",
      "Confusion Matrix:\n",
      "[[88 31  0  0  0]\n",
      " [17 64  0  0  0]\n",
      " [ 6 31  0  0  0]\n",
      " [ 5 28  0  0  0]\n",
      " [ 1  5  0  0  0]]\n",
      "----------------------------------------\n",
      "Evaluating K-Nearest Neighbors on test set...\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.81      0.78       119\n",
      "           1       0.46      0.47      0.46        81\n",
      "           2       0.29      0.32      0.31        37\n",
      "           3       0.33      0.21      0.26        33\n",
      "           4       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.55       276\n",
      "   macro avg       0.37      0.36      0.36       276\n",
      "weighted avg       0.54      0.55      0.55       276\n",
      "\n",
      "Confusion Matrix:\n",
      "[[96 17  4  2  0]\n",
      " [19 38 15  8  1]\n",
      " [ 4 14 12  4  3]\n",
      " [ 5 12  8  7  1]\n",
      " [ 2  2  2  0  0]]\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "best_models = {}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    print(f\"Tuning hyperparameters for {model_name} using RandomizedSearchCV...\")\n",
    "    \n",
    "    clf = Pipeline(steps=[('classifier', model)])\n",
    "    \n",
    "    random_search = RandomizedSearchCV(\n",
    "        estimator=clf,\n",
    "        param_distributions=param_distributions[model_name],\n",
    "        scoring=make_scorer(roc_auc_score, needs_proba=True),\n",
    "        n_iter=10, \n",
    "        cv=5,\n",
    "        random_state=42\n",
    "    )\n",
    "    \n",
    "    random_search.fit(X_train_transformed, y_train)\n",
    "    \n",
    "    best_models[model_name] = random_search.best_estimator_\n",
    "    \n",
    "    print(f\"Best parameters for {model_name}: {random_search.best_params_}\")\n",
    "    print(\"-\" * 40)\n",
    "\n",
    "for model_name, model in best_models.items():\n",
    "    print(f\"Evaluating {model_name} on test set...\")\n",
    "    y_pred = model.predict(X_test_transformed)\n",
    "    \n",
    "    print(\"Classification Report:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(\"-\" * 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "{bibliography}\n",
    "```"
   ]
  }
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