{"id":146625,"date":"2025-08-18T12:47:55","date_gmt":"2025-08-18T07:17:55","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146625"},"modified":"2025-08-18T12:47:55","modified_gmt":"2025-08-18T07:17:55","slug":"heart-disease-predictor-using-ann-part-2","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/heart-disease-predictor-using-ann-part-2\/","title":{"rendered":"Deep Learning Project \u2013 Heart Disease Predictor using ANN Part &#8211; 2"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1G1pxjU9OwCnFBnpwOedf5hiiTXoNav6L\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Heart Disease Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">Heart Disease Predictor using ANN (Keras)\r\n\r\nWe want to predict whether a person has heart disease or not based on multiple health parameters. This is a binary classification problem (0 = No disease, 1 = Disease).\r\n| Column   | Description                                             |\r\n| -------- | ------------------------------------------------------- |\r\n| age      | Age                                                     |\r\n| sex      | 1 = male; 0 = female                                    |\r\n| cp       | Chest pain type (0-3)                                   |\r\n| trestbps | Resting blood pressure                                  |\r\n| chol     | Serum cholesterol                                       |\r\n| fbs      | Fasting blood sugar (&gt; 120 mg\/dl) (1 = true; 0 = false) |\r\n| restecg  | Resting electrocardiographic results                    |\r\n| thalach  | Maximum heart rate achieved                             |\r\n| exang    | Exercise induced angina                                 |\r\n| oldpeak  | ST depression induced by exercise                       |\r\n| slope    | Slope of the peak exercise ST segment                   |\r\n| ca       | Major vessels (0-3) colored by fluoroscopy              |\r\n| thal     | 1 = normal; 2 = fixed defect; 3 = reversible defect     |\r\n| target   | 0 = no disease, 1 = disease                             |\r\n\r\n\r\n# Import Libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom sklearn.metrics import confusion_matrix, classification_report\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\n\r\n#  Load Dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/heart\/heart.csv\")\r\ndf.isnull().sum()\r\n\r\n# Data Preprocessing\r\n\r\n# Split features and target\r\nX = df.drop('target', axis=1)\r\ny = df['target']\r\nX.head()\r\ny.head()\r\n\r\ndf.shape\r\n\r\n# Scale features between 0 and 1\r\nscaler = MinMaxScaler()\r\nX_scaled = scaler.fit_transform(X)\r\n\r\n# Split into train-test\r\nX_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\r\nlen(X_train)\r\nlen(X_test)\r\n\r\n#  Build ANN Model\r\nmodel = Sequential()\r\n# Input layer\r\nmodel.add(Dense(32, activation='relu', input_shape=(X.shape[1],))) # Input + First Hidden\r\n# Hidden layers\r\nmodel.add(Dense(16, activation='relu')) # Second Hidden\r\nmodel.add(Dense(8, activation='relu')) # Thrid Hidden\r\n# Output layer (binary classification)\r\nmodel.add(Dense(1, activation='sigmoid')) # Output\r\n# Compile the model\r\nmodel.compile(\r\n  optimizer='adam',             # How model learns\r\n  loss='binary_crossentropy',  # How model calculates error\r\n  metrics=['accuracy']         # What performance you want to track\r\n)\r\n\r\n#  Train the model\r\nhistory = model.fit(X_train, y_train, validation_split=0.2, epochs=100, verbose=0)\r\n\r\n#  Plot Loss Graph\r\nplt.plot(history.history['loss'], label='Training Loss')\r\nplt.plot(history.history['val_loss'], label='Validation Loss')\r\nplt.title('Loss Over Epochs')\r\nplt.xlabel('Epochs')\r\nplt.ylabel('Loss')\r\nplt.legend()\r\nplt.show()\r\n\r\n#  Evaluate Model\r\nloss, accuracy = model.evaluate(X_test, y_test)\r\nprint(f\"\\nTest Accuracy: {accuracy*100:.2f}%\")\r\n\r\n#  Predictions\r\ny_pred = model.predict(X_test)\r\ny_pred\r\ny_pred_classes = (y_pred &gt; 0.5).astype(int)\r\ny_pred_classes\r\n\r\n#  Confusion Matrix &amp; Classification Report\r\ncm = confusion_matrix(y_test, y_pred_classes)\r\nprint(\"\\nConfusion Matrix:\\n\", cm)\r\ncr = classification_report(y_test, y_pred_classes)\r\nprint(\"\\nClassification Report:\\n\", cr)\r\n\r\n#  Visualize Confusion Matrix\r\nplt.figure(figsize=(5, 5))\r\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\r\nplt.xlabel('Predicted')\r\nplt.ylabel('Actual')\r\nplt.title('Confusion Matrix')\r\nplt.show()\r\n\r\nnew_data = np.array([[48,1,1,90,123,1,0,162,0,1.1,2,1,1]])\r\nnew_data_scaled = scaler.transform(new_data)\r\nprediction = model.predict(new_data_scaled)\r\nresult = (prediction &gt; 0.5).astype(int)\r\nprint(\"Heart Disease Present?\" , result)\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:14,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1G1pxjU9OwCnFBnpwOedf5hiiTXoNav6L\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[],&quot;broken&quot;:false,&quot;last_checked&quot;:null,&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Heart Disease Dataset Heart Disease Predictor using ANN (Keras) We want to predict whether a person has heart disease or not based on multiple health parameters. This is a binary classification problem&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[21686,35086,34981,34982,35085,34983,8431,33127,33128,20697],"class_list":["post-146625","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-deep-learning-project","tag-heart-disease-prediction-using-ann","tag-heart-disease-predictor","tag-heart-disease-predictor-using-ann","tag-heart-disease-predictor-using-deep-learning","tag-heart-disease-predictor-using-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Deep Learning Project \u2013 Heart Disease Predictor using ANN Part - 2 - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/heart-disease-predictor-using-ann-part-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning Project \u2013 Heart Disease Predictor using ANN Part - 2 - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 Heart Disease Dataset Heart Disease Predictor using ANN (Keras) We want to predict whether a person has heart disease or not based on multiple health parameters. 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