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Cardiac Disease prediction using Machine Learning, Flask and MongoDB

This repository contains for a simple flask application that uses ML algorithm to classify whether a person has cardiac disease or not.

Dataset

Dataset can be collected from UCL ML Repository.

Data preprocessing

The Data has to preprocessed extensively. As some information cannot be easily inputted some of the columns were dropped.

Training & Testing

Various ML algorithms were used for training. Currently SVM is used in this application. The model performs moderately with around 72 % accuracy.

Flask Application

A Simple Flask application was created with a UI so as collect the response from the user. Once collected it goes into the app.py file through which the ML algorithm will predict whether the person is having Cardiac disease or not.

Backend

MongoDB is the backend here. All the data gets stored into Mongo Cluster in the cloud. In this way the data can used for retraining and other evaluation purpose.

Deployment

The application was deployed in Heroku. However taken down now due to some updates in their server.

App Overview

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