Train Heart Disease Detection Model

011/exercise/readme.md

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+Learning Goals:
+
+Understand the concept and use of the Random Forest Classifier and K-Nearest Neighbors (KNN) algorithm.
+Learn how to preprocess medical data for machine learning, particularly in the context of heart disease detection.
+Explore feature selection and model optimization techniques.
+Evaluate model performance using metrics such as accuracy,cross_val_score.
+Gain proficiency in using scikit-learn for implementing and comparing machine learning models.
+Exercise Statement:
+
+Build a machine learning model to detect whether a person is suffering from heart disease or not.
+Implement the model using two different algorithms: Random Forest Classifier and K-Nearest Neighbors (KNN).
+Compare the performance of these models and choose the one that gives the best results based on evaluation metrics.
+Prerequisites:
+
+Familiarity with Random Forest Classifier and KNN algorithms.
+Knowledge of basic machine learning concepts, such as feature scaling, cross-validation, and hyperparameter tuning.
+Familiarity with scikit-learn for model building and evaluation.
+Prior experience with medical datasets will be helpful.
+Data Source/Summary:
+
+The dataset used in this exercise typically involves patient data containing attributes such as age, gender, cholesterol levels, blood pressure, etc., to predict whether a person has heart disease.

011/solution/main.py

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+from ucimlrepo import fetch_ucirepo
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import pandas as pd
+
+from sklearn.preprocessing import LabelEncoder
+
+
+# Fetch dataset (Heart Disease dataset)
+heart_disease = fetch_ucirepo(id=45)
+
+
+# Access dataframe if available
+if hasattr(heart_disease, 'dataframe') and heart_disease.dataframe is not None:
+    heart_disease_df = heart_disease.dataframe
+else:
+    print("Dataframe not available, loading raw data.")
+    # Manually handling the raw data (assuming 'features' and 'targets' are available)
+    features = heart_disease.data.features
+    target = heart_disease.data.targets
+
+    # Create DataFrame
+    heart_disease_df = pd.DataFrame(features, columns=heart_disease.data.feature_names)
+    heart_disease_df['target'] = target
+
+# # Check for missing values
+# print(heart_disease_df.isnull().sum())
+
+# Option 1: Fill missing values with the median (for numerical columns)
+heart_disease_df['ca'].fillna(heart_disease_df['ca'].median(), inplace=True)
+heart_disease_df['thal'].fillna(heart_disease_df['thal'].mode()[0], inplace=True)
+
+# # Option 2: Drop rows with missing values (if appropriate)
+# heart_disease_df.dropna(inplace=True)
+
+
+# Encode categorical columns (assuming 'sex', 'cp', 'restecg', etc. are categorical)
+le = LabelEncoder()
+
+# Apply label encoding to categorical columns
+heart_disease_df['sex'] = le.fit_transform(heart_disease_df['sex'])
+heart_disease_df['cp'] = le.fit_transform(heart_disease_df['cp'])
+heart_disease_df['fbs'] = le.fit_transform(heart_disease_df['fbs'])
+heart_disease_df['restecg'] = le.fit_transform(heart_disease_df['restecg'])
+heart_disease_df['exang'] = le.fit_transform(heart_disease_df['exang'])
+heart_disease_df['slope'] = le.fit_transform(heart_disease_df['slope'])
+heart_disease_df['thal'] = le.fit_transform(heart_disease_df['thal'])
+
+
+
+# Split the data into features (X) and target (y)
+X = heart_disease_df.drop('target', axis=1)  # Features
+y = heart_disease_df['target']  # Target variable
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize the Random Forest Classifier model
+rf_model = RandomForestClassifier(random_state=42)
+
+# Train the model on the training data
+rf_model.fit(X_train, y_train)
+
+# Make predictions on the test set
+y_pred = rf_model.predict(X_test)
+
+# # Evaluate the model
+# accuracy = accuracy_score(y_test, y_pred)
+# print(f"Random Forest Model Accuracy: {accuracy:.4f}")
+
+# # Optional: Print feature importances to understand which features are most important
+# print("Feature Importances:")
+# for feature, importance in zip(X.columns, rf_model.feature_importances_):
+#     print(f"{feature}: {importance:.4f}")
+
+
+# # Sort features by their importance
+feature_importance = pd.DataFrame({'Feature': X.columns, 'Importance': rf_model.feature_importances_})
+feature_importance = feature_importance.sort_values(by='Importance', ascending=False)
+
+# Print the sorted features and their importance scores
+# print(feature_importance)
+
+
+# Select top 5 most important features
+top_n_features = feature_importance['Feature'].head(5)
+
+# Filter the dataset to only include top N features
+X_top5 = X[top_n_features]
+
+# Train the model using the selected top 5 features
+rf_model.fit(X_top5, y)
+
+# Evaluate the model's performance
+y_pred = rf_model.predict(X_test[top_n_features])
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Random Forest Model Accuracy with Top 5 Features: {accuracy:.4f}")