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❤️ Heart Disease Prediction

Overview :

This project builds a machine learning model to predict the presence of heart disease based on clinical and lifestyle features. It supports early diagnosis and risk stratification for patients, helping healthcare providers make informed decisions.

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Dataset Description :

The dataset includes patient-level data with various diagnostic and demographic attributes.

Key columns:

• Age: Age of the patient
• Sex: Gender (1 = male, 0 = female)
• ChestPainType: Type of chest pain (e.g., typical angina, asymptomatic)
• RestingBP: Resting blood pressure (mm Hg)
• Cholesterol: Serum cholesterol (mg/dl)
• FastingBS: Fasting blood sugar > 120 mg/dl (1 = true, 0 = false)
• RestingECG: Resting electrocardiographic results
• MaxHR: Maximum heart rate achieved
• ExerciseAngina: Exercise-induced angina (1 = yes, 0 = no)
• Oldpeak: ST depression induced by exercise
• ST_Slope: Slope of the peak exercise ST segment
• HeartDisease: Target variable (1 = disease present, 0 = no disease)

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Workflow Summary :

1. Data Loading

df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')

2. Preprocessing

df = df.dropna()
df_encoded = pd.get_dummies(df, drop_first=True)
X = df_encoded.drop('HeartDisease', axis=1)
y = df_encoded['HeartDisease']

3. Train-Test Split

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)

4. Model Training

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

5. Evaluation

y_pred = model.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

6. Prediction on New Patient

input_df = pd.DataFrame(np.zeros((1, len(X_train.columns))), columns=X_train.columns)
# Fill in values based on confirmed X_train.columns
model.predict(input_df)

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Performance Metrics :

• Accuracy: ~90–95% on test data
• Robust classification across age, cholesterol, and ECG features
• Top predictors: Chest pain type, ST slope, MaxHR, and ExerciseAngina

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Dependencies :

numpy
pandas
scikit-learn

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Author: Sushree Bandita Das