Heart Disease Prediction API

This repository contains source code of our project for Provathon. Heart Disease Prediction API is Machine Learning based solution to predict risks of heart disease using a integrable API.

Description

Our solution is based on microservices architecture and the AI Model can be independently used in any other platform with ease. We've also included a demo dashboard of our smart watch simulator where we can receive patient's real time health data directly from his smart watch and use those value to get a realtime prediction of immediate risks of heart attacks in a patient. This real time prediction is also simulated in a continuous graph to warn the patients in case of any risks.
In similar way, our api can be integrated with any smart watches, hospital management system, health apps to get a real time prediction of immediate risks of heart attacks.

Technical Aspect

Response

API Endpoint: http://18.215.165.176:8081/predict

{
  "status":"success",
  "value":"92",
  "risk":true,
}

status : If any error occured in backend it will say error else it will be set to success.
value : Confidence measure of the AI Model.
risk : boolean value which says if the risk of heart disease is high ( default threshold is 75% )

Installation and Test on Local Machine

Step 1: Installation of WebApp.

git clone https://github.com/Romansth/heart-disease-api.git
cd heart-disease-api/SmartWatch_Dashboard
pip install django requests numpy pickle django-cors-headers 
python manage.py runserver

Step 2: Installation of AI Model

cd heart-disease-api/api
pip install flask pickle numpy flask-cors
python Deployed_Flask_App.py

Step 3: Run Smartwatch Simulator

cd heart-disease-api
python smartwatch_simulator.py

Step 4: Go to localhost:8000/patient

After following all these steps, the demo app will successfully run on your local machine.

Feel free to contribute !