Heart Disease Prediction and Monitoring API

Overview

The Heart Disease Prediction and Monitoring API is a robust backend service built using FastAPI. This API leverages various machine learning algorithms to predict the likelihood of heart disease in patients. After testing multiple algorithms, we optimized our model using Random Forest to achieve the highest accuracy, further refining it for enhanced performance.

Features

• Multiple Machine Learning Algorithms:

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Support Vector Machine (SVM)
  • Naive Bayes
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Neural Networks

• High Accuracy: The final model employs Random Forest, refined to maximize predictive accuracy of 97%.

• Easy Integration: Designed for seamless integration into applications for heart disease prediction.

Technologies Used

• Backend Framework: FastAPI
• Machine Learning Libraries:
  • Scikit-learn
  • Pandas
  • NumPy
  • TensorFlow/Keras (for Neural Networks)
  • Matplotlib/Seaborn (for visualization)
• Data Handling: Pandas and NumPy for data manipulation

Installation

1. Clone the Repository:

   git clone <repository-url>
   cd heart-disease-prediction-api

2. Install Dependencies:

pip install -r requirements.txt

3. **Run the API **:

uvicorn main:app --reload

Usage

The API exposes endpoints to input patient data and receive heart disease predictions.

Example Request

POST /predict
Content-Type: application/json

{
  "age": 54,
  "sex": 1,
  "cp": 2,
  "trestbps": 140,
  "chol": 250,
  "fbs": 0,
  "restecg": 1,
  "thalach": 150,
  "exang": 0,
  "oldpeak": 1.5,
  "slope": 2,
  "ca": 0,
  "thal": 2,
  "target": 1
}

Example Response

Copy code
{
  "prediction": 1,
  "probability": 0.85
}

Project Highlights

This project exemplifies the ability to integrate machine learning with modern web technologies, providing an accessible solution for heart disease prediction. The use of FastAPI ensures high performance and scalability, making it suitable for real-time applications.

Contributing

We welcome contributions! To contribute:

Fork the repository.

• Create a new branch (git checkout -b feature/YourFeature).
• Make your changes and commit them (git commit -m 'Add your feature').
• Push to the branch (git push origin feature/YourFeature).
• Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.