Heart Disease Prediction System
A full-stack medical AI web application for cardiovascular health risk assessment using Machine Learning and a modern React frontend.
Project Overview
The Heart Disease Prediction System is a full-stack medical AI web application that predicts the likelihood of heart disease in a patient based on 11 clinical parameters. The system uses a supervised Machine Learning model trained on a real-world cardiac dataset and exposes the AI through a professional REST API backend.
Architecture Overview
The project uses a decoupled Client-Server architecture:
React UI
TypeScript + Vite
Flask API
Python Backend
ML Model
Random Forest Classifier
Machine Learning Model
Algorithm Used: Random Forest Classifier (~88% accuracy). Random Forest is an ensemble machine learning method that builds multiple Decision Trees during training and merges their predictions.
- ✦Handles both numerical and categorical medical data efficiently.
- ✦Robust performance on clinical tabular datasets.
- ✦Provides higher accuracy than a single Decision Tree.
- ✦Interpretability is reasonable for Healthcare AI.
Key Features
The application is packed with clinical and developer features:
AI Heart Disease Prediction powered by a trained Random Forest Classifier.
PDF Report Generation via ReportLab for formatted clinical reports.
Interactive Health Dashboard with Bar Charts and Radar Charts comparing vital signs against healthy averages.
Offline Body Mass Index (BMI) calculator with automatic categorization.
Tech Stack
Built using a modern decoupled stack:
React 18 + TypeScript + Vite (Frontend)
Chart.js + react-chartjs-2 (Data Visualization)
Python + Flask + Flask-CORS (Backend REST API)
Scikit-learn + Pandas + NumPy (Machine Learning)
ReportLab (PDF Generation)