AI-Based Chronic Disease Risk Predictor

Problem statement

Chronic diseases like diabetes and heart disease are rising globally. Early detection can significantly reduce complications, yet many patients undergo checkups infrequently. A system that can assess health risk using simple medical parameters would encourage preventive healthcare.

Abstract

This project builds a machine learning model that predicts disease risk levels using medical and lifestyle data. Popular algorithms such as Random Forest, XGBoost, and Logistic Regression are compared. A web dashboard allows users to enter data such as glucose level, BMI, blood pressure, and activity level. The model outputs a probability score indicating disease risk. The system can be integrated with hospital tools to assist doctors in early detection.

Components required

• Python
• Pandas, NumPy, Scikit-Learn
• Medical Dataset (UCI Repository)
• Flask or FastAPI Web Backend
• React / HTML UI
• Cloud Deployment (optional)

Block diagram

Working

Users enter medical and lifestyle information into the system. This data undergoes preprocessing steps such as normalization, missing value handling, and feature extraction. A trained ML model predicts the risk score of different diseases. The dashboard displays results with color-coded risk zones and suggestions for improvement. The system can also store historical data to show health trends over time.

Applications

• Hospitals and clinics
• Personal health monitoring apps
• Insurance companies
• Corporate employee wellness programs