- Flask REST API which predicts the probability of Coronary Heart Disease in a patient taking 9 different parameters based on the patient's history as input.
- The API uses a Logistic Regression Model from sci-kit-learn trained on the Framingham Heart Study Dataset from Kaggle.
- The model achieved a test accuracy of around 88%.
- Deployed on OnRender: Onrender Link
- View the Jupyter Notebook: Jupyter Notebook
- Flask REST API: API
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Takes 9 parameters as input
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Returns a binary prediction (0 or 1) and probability as well.
https://cardio-care-predictor-api.onrender.com/predict?age=31&sex=1&cigs=5&chol=230&sBP=280&dia=0&dBP=90&gluc=87&hRate=84{ "data":{ "age": "31", "cigsPerDay": "5", "diaBP": "90", "diabetes": "0", "glucose": "87", "heartRate": "84", "sex": "1", "sysBP": "280", "totChol": "230" }, "prediction":[ 1 ], "probability":[ [ 0.4587093009776524, 0.5412906990223476 ] ] }
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Returns the model details such as intercept and coefficients.
https://cardio-care-predictor-api.onrender.com/model
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Clone the repository
git clone https://github.com/amansrv/Cardio-Care-Predictor-API.git cd Cardio-Care-Predictor-API -
Install dependencies
pip install requirements.txt
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Start the Flask server
python3 app.py
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