❤️ Heart Disease Prediction Using Machine Learning

A machine learning model to predict heart disease using logistic regression. This project analyzes medical indicators to assist in early detection of potential heart conditions.

🎯 Performance Metrics

• Training Accuracy: 85.24%
• Test Accuracy: 80.49%
• Model Status: Good generalization with minimal overfitting
• Prediction Type: Binary classification (0: No Heart Disease, 1: Heart Disease)

📊 Dataset

• Source: Kaggle Heart Disease Dataset
• Records: 1025 entries
• Features: 14 (13 input features + 1 target)
• Distribution:

  • Healthy (0): 499 cases

  • Heart Disease (1): 526 cases

    Feature	Description	Range/Type
    age	Age of the patient	29-77 years
    sex	Gender	0: Female, 1: Male
    cp	Chest pain type	0-3
    trestbps	Resting blood pressure	94-200 mm Hg
    chol	Serum cholesterol	126-564 mg/dl
    fbs	Fasting blood sugar > 120 mg/dl	0: False, 1: True
    restecg	Resting ECG results	0-2
    thalach	Maximum heart rate achieved	71-202
    exang	Exercise induced angina	0: No, 1: Yes
    oldpeak	ST depression induced by exercise	0-6.2
    slope	Slope of peak exercise ST segment	0-2
    ca	Number of major vessels colored by fluoroscopy	0-4
    thal	Thalassemia	0-3

🛠️ Installation

1. Clone the repository:

git clone https://github.com/yourusername/heart-disease-prediction.git
cd heart-disease-prediction

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

💻 Usage

Load and Prepare Data

import pandas as pd
from sklearn.model_selection import train_test_split

# Load data
heart_data = pd.read_csv('heart.csv')

# Split features and target
X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']

# Split into training and test sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)

Train Model

from sklearn.linear_model import LogisticRegression

# Initialize and train model
model = LogisticRegression()
model.fit(X_train, Y_train)

Make Predictions

def predict_heart_disease(input_data):
    # Example input: (43, 0, 0, 132, 341, 1, 0, 136, 1, 3, 1, 0, 3)
    input_array = np.asarray(input_data).reshape(1, -1)
    prediction = model.predict(input_array)
    return "Heart Disease Detected" if prediction[0] == 1 else "No Heart Disease Detected"

📈 Results

# Model Evaluation
print(f'Training Accuracy: {accuracy_score(model.predict(X_train), Y_train):.2%}')
print(f'Testing Accuracy: {accuracy_score(model.predict(X_test), Y_test):.2%}')

🔄 Future Improvements

• Implement feature scaling
• Add cross-validation
• Try different algorithms (Random Forest, SVM)
• Add feature importance analysis
• Include ROC curve analysis
• Add confusion matrix visualization
• Perform hyperparameter tuning

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Dataset provided by Kaggle
• Inspiration from various heart disease research papers
• scikit-learn documentation and community

📧 Contact

Author - Vislavath Pavani ♡

Deployed Link: (https://mybinder.org/v2/gh/12pavani/Heart-Disease-Prediction-Model.git/main)](https://mybinder.org/v2/gh/12pavani/Heart-Disease-Prediction-Model/69879853fe0cf7be0cad78b563af53f007784901?urlpath=lab%2Ftree%2FHeart%20Disease%20Prediction.ipynb)