Machine Learning

Heart Disease Risk Analyzer

Machine learning system for the predictive assessment of heart disease, achieving 88.6% accuracy and providing an instantaneous risk analysis.

View on GitHub Live Demo

Executive Summary

This project addresses the need for rapid and accessible preliminary health screenings by developing and deploying an AI-powered web application that provides an instantaneous risk assessment for heart disease. The tool serves as an efficient, data-driven first step for individuals to evaluate their cardiovascular health profile.
The application is built on a K-Nearest Neighbors (KNN) machine learning model, which achieves a validated accuracy of 88.6%. The interactive user interface was developed using Streamlit and features a real-time risk visualization gauge powered by Plotly. The complete end-to-end project is version-controlled with Git/GitHub and deployed live on Streamlit Cloud, demonstrating a robust and modern MLOps workflow.

The key impact of this project is the drastic reduction in assessment time, transforming a traditionally lengthy process into an immediate insight. The result is a fully functional, user-friendly tool that makes proactive health monitoring more accessible and empowers users to make informed decisions promptly.

Technology Stack

Python Pandas Matplotlib Numpy Seaborn Supervise Learning Streamlit scikit-learn

Problem Statement & Business Context

This project tackles the critical delay in heart disease screening by providing an instant, AI-powered risk assessment. It closes the gap between public health needs and accessible digital tools, empowering users to proactively manage their health and enabling more efficient healthcare triage.

  • Reduces Screening Time: Provides an instant risk analysis, eliminating traditional wait times.
  • Increases Accessibility:: Offers a free, on-demand tool for immediate preliminary screening.
  • Encourages Proactive Health: Empowers users with early insights before symptoms arise.
  • Improves Triage: Helps prioritize healthcare resources toward high-risk individuals.

This app serves to augment, not replace, a formal diagnosis. It offers an instant, preliminary screening that highlights potential risk, empowering users to seek professional medical review.