Pankajkaushik2207/Cardio-care-Predictor-Api

Jupyter Notebook

Cardio Care Predictor API

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%.

Useful Links

Deployed on OnRender: Onrender Link
View the Jupyter Notebook: Jupyter Notebook
Flask REST API: API

Predict Endpoint

Takes 9 parameters as input

Returns a binary prediction (0 or 1) and probability as well.

Sample query

  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

Sample output

{
   "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
      ]
   ]
}

Model Endpoint

Returns the model details such as intercept and coefficients.
```
  https://cardio-care-predictor-api.onrender.com/model
```

Running locally

Clone the repository

   git clone https://github.com/Pankajkaushik2207/Cardio-care-Predictor-Api.git
 
   cd Cardio-Care-Predictor-API

Install dependencies
```
    pip install requirements.txt
```
Start the Flask server
```
    python3 app.py
```