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Heart Disease Prediction – ML Web Application
A machine learning web application that predicts heart disease risk using patient health inputs, built with Python, Flask, Scikit-learn, and MySQL.
The Problem
Early detection of heart disease risk can save lives, but accessing clinical risk assessments is often slow and expensive. A patient-facing web tool can help with preventive healthcare.
The Solution
Designed and implemented a web application that takes user health inputs (age, blood pressure, cholesterol levels) and returns a risk prediction. Built a Flask backend, integrated Scikit-learn ML models, used MySQL for data persistence, and tested all APIs with Postman.
The Outcome
Delivered an accurate risk assessment tool that enhances early detection and preventive healthcare through an accessible web interface backed by a validated ML pipeline.
Project Details
Heart Disease Prediction is a machine learning web application built with Python, Flask, Scikit-learn, Pandas, and MySQL. Users input health metrics (age, blood pressure, cholesterol, etc.) and receive a heart disease risk prediction from the ML model. The backend handles data persistence in MySQL, applies the trained Scikit-learn model for inference, and exposes REST APIs tested with Postman.
Technologies
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