Heart Disease Prediction App
This app allows users to input patient attributes such as age, chest pain type, cholesterol, and more. The backend uses a Random Forest model to predict whether the patient is likely to have heart disease.
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Deployment & Code
Project Snapshot
Core Stack
ML
Flask
Feature Selection
Render
Python
Overview
A web application for predicting the presence of heart disease using a trained Random Forest machine learning model. As a input the model takes 8 attributes to perform prediction. The features names are: [Age, Chest Pain Type, Resting Blood Pressure, Cholesterol, Max Heart Rate, Oldpeak, Number of Major Vessels, Thalassemia]. The Random Forest model achieves an accuracy of 99% with 8 selected features. As a feature selection RFE(Recursive Feature Elimination) method. Built with Flask, this app provides a user-friendly interface for entering patient data and viewing prediction results.