Heart Disease Prediction.ipynb

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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <th>restecg</th>\n",
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       "      <th>ca</th>\n",
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      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "40    65    0   4       150   225    0        2      114      0      1.0   \n",
       "130   51    1   3        94   227    0        0      154      1      0.0   \n",
       "32    64    1   3       140   335    0        0      158      0      0.0   \n",
       "74    44    1   4       110   197    0        2      177      0      0.0   \n",
       "158   68    1   3       118   277    0        0      151      0      1.0   \n",
       "\n",
       "     slope  ca  thal  num  \n",
       "40       2   3     7    1  \n",
       "130      1   1     7    0  \n",
       "32       1   0     3    1  \n",
       "74       1   1     3    1  \n",
       "158      1   1     7    0  "
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   "source": [
    "\n",
    "# for data analysis\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import random as rnd\n",
    "import sys\n",
    "\n",
    "# for data viz\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline \n",
    "\n",
    "# for machine learning \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "train_data = pd.read_csv('G:/health/Heart-Disease-Prediction-Machine-Learning-master/processed_cleveland_data_train.csv')\n",
    "test_data = pd.read_csv('G:/health/Heart-Disease-Prediction-Machine-Learning-master/processed_cleveland_data_test.csv')\n",
    "\n",
    "# Parameter of interest is num, for now this is a binary problem\n",
    "def make_prediction_var_binary(df):\n",
    "    df['num'] = df['num'].replace([1, 2, 3, 4, 5, 6], 1)\n",
    "\n",
    "        \n",
    "make_prediction_var_binary(train_data)\n",
    "make_prediction_var_binary(test_data)\n",
    "\n",
    "train_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from numpy import genfromtxt\n",
    "dataset = genfromtxt('G:/health/prediction-of-heart-disease-using-neural-network-master/heart.csv',dtype = float, delimiter=',')\n",
    "#print dataset\n",
    "X = dataset[:,0:12] #Feature Set\n",
    "y = dataset[:,13]   #Label Set\n",
    "\n",
    "X_train = train_data.drop(['num'], axis=1)\n",
    "y_train = train_data['num']\n",
    "X_test = test_data.drop(['num'], axis=1)\n",
    "y_test = test_data['num']\n",
    "#X_train , y_train , X_test , y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
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     "text": [
      "C:\\Users\\Sarvesh Angadi\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\Sarvesh Angadi\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(14, 14, 14), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import make_scorer, accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Choose classifer \n",
    "\n",
    "clf = MLPClassifier()\n",
    "\n",
    "# Choose Parameters and stuff\n",
    "parameters = {'solver': ['lbfgs'],\n",
    "             'alpha':[1e-4],\n",
    "             'hidden_layer_sizes':[(14),(14,14,14)],\n",
    "             'random_state': [1]}\n",
    "\n",
    "# Type of scoring to compare parameter combos \n",
    "acc_scorer = make_scorer(accuracy_score)\n",
    "\n",
    "# Run grid search \n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=acc_scorer)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Pick the best combination of parameters\n",
    "clf = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data \n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7868852459016393\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.90      0.81        30\n",
      "           1       0.88      0.68      0.76        31\n",
      "\n",
      "   micro avg       0.79      0.79      0.79        61\n",
      "   macro avg       0.80      0.79      0.78        61\n",
      "weighted avg       0.80      0.79      0.78        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "all_data = pd.read_csv('G:/health/Heart-Disease-Prediction-Machine-Learning-master/processed_cleveland_data.csv')\n",
    "make_prediction_var_binary(all_data)\n",
    "X_all = all_data.drop(['num'], axis=1)\n",
    "y_all = all_data['num']\n",
    "\n",
    "predictions = clf.predict(X_test)\n",
    "print(accuracy_score(y_test, predictions))\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=10, random_state=100, shuffle=False)\n",
      "Fold 1 accuracy: 0.9\n",
      "Mean Accuracy: 0.9\n",
      "Fold 2 accuracy: 0.8333333333333334\n",
      "Mean Accuracy: 0.8666666666666667\n",
      "Fold 3 accuracy: 0.8\n",
      "Mean Accuracy: 0.8444444444444444\n",
      "Fold 4 accuracy: 0.9333333333333333\n",
      "Mean Accuracy: 0.8666666666666667\n",
      "Fold 5 accuracy: 0.8\n",
      "Mean Accuracy: 0.8533333333333333\n",
      "Fold 6 accuracy: 0.7333333333333333\n",
      "Mean Accuracy: 0.8333333333333334\n",
      "Fold 7 accuracy: 0.7333333333333333\n",
      "Mean Accuracy: 0.819047619047619\n",
      "Fold 8 accuracy: 0.8620689655172413\n",
      "Mean Accuracy: 0.8244252873563218\n",
      "Fold 9 accuracy: 0.6896551724137931\n",
      "Mean Accuracy: 0.8094508301404854\n",
      "Fold 10 accuracy: 0.7931034482758621\n",
      "Mean Accuracy: 0.8078160919540232\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kf = KFold(n_splits=10 , random_state =100)\n",
    "kf.get_n_splits(all_data)\n",
    "outcomes=[]\n",
    "print(kf)  \n",
    "fold=0\n",
    "\n",
    "for train_index, test_index in kf.split(all_data):\n",
    "   fold+=1\n",
    "   #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "   X_train, X_test = X_all.values[train_index], X_all.values[test_index]\n",
    "   y_train, y_test = y_all.values[train_index], y_all.values[test_index]\n",
    "   clf.fit(X_train, y_train)\n",
    "   predictions = clf.predict(X_test)\n",
    "   accuracy = accuracy_score(y_test, predictions)\n",
    "   outcomes.append(accuracy)\n",
    "   print(\"Fold {0} accuracy: {1}\".format(fold, accuracy)) \n",
    "   mean_outcome = np.mean(outcomes)\n",
    "   print(\"Mean Accuracy: {0}\".format(mean_outcome)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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