cardio-disease-model

Cardio Disease Prediction Model

A machine learning–powered Cardio Disease Prediction system using Random Forest and Logistic Regression, deployed with FastAPI and hosted on Hugging Face + Render.

This project predicts the risk of cardiovascular disease based on patient health parameters and provides both probability scores and risk classification.

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Live API Demo

Base URL: https://cardio-fastapi-8ijy.onrender.com

Swagger UI: https://cardio-fastapi-8ijy.onrender.com/docs

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Models Used

• Random Forest Classifier
  • Handles non-linear relationships
  • Robust against noise
• Logistic Regression
  • Interpretable probabilities
  • Baseline medical risk model

Both models are trained on a cleaned cardiovascular dataset.

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Repository Contents

cardio-disease-model/
│
├── random_forest_model.pkl     # Trained Random Forest model
├── logistic_weights.npy        # Logistic regression weights
├── logistic_bias.npy           # Logistic regression bias
├── scaler_num.pkl              # Numerical feature scaler
├── scaler_int.pkl              # Categorical feature scaler
└── README.md

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Input Features

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API Endpoints

Health Check

GET /health

Random Forest Prediction

POST /predict/randomforest

Logistic Regression Prediction

POST /predict/logistic

Compare Both Models

POST /predict/compare

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Example Request

{
  "age": 55,
  "gender": 1,
  "height": 170,
  "weight": 72,
  "ap_hi": 130,
  "ap_lo": 85,
  "cholesterol": 1,
  "gluc": 1,
  "smoke": 0,
  "alco": 0,
  "active": 1
}

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Example Response

{
  "model": "Random Forest",
  "prediction": 0,
  "probability": 0.15,
  "risk": "Low Risk"
}

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Disclaimer

This project is for educational and research purposes only. It must not be used as a substitute for professional medical diagnosis.

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Tech Stack

• Python
• Scikit-learn
• NumPy
• FastAPI
• Uvicorn
• Hugging Face Hub
• Render Cloud

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License

This project is licensed under the MIT License.

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Author

Vishal Baraiya B.Tech CSE | Machine Learning & Full Stack Developer

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