An AI-Based Smart Health Monitoring System for Multi-Disease Risk Prediction Using Machine Learning

Abstract

Chronic illnesses like diabetes and heart disease continue to place a heavy burden on global health, making early detection more important than ever. This study introduces an artificial intelligence framework designed to predict the risk of both conditions while keeping user data private. Using Random Forest ensemble learning, the system analyzes two well-known datasets: the PIMA Indians Diabetes dataset and the Cleveland Heart Disease dataset. To ensure reliable results, the researchers applied stratified train-test splitting and five-fold cross-validation.The diabetes model reached an accuracy of nearly 76% with a strong ROC-AUC score of 0.813, while the heart disease model performed even better, achieving over 81% accuracy and a ROCAUC of 0.947. Importantly, the framework doesn’t just provide predictions—it also highlights which features matter most, aligning with established medical risk factors. This makes the system more transparent and clinically meaningful.Compared to traditional methods like Logistic Regression and Decision Trees, the ensemble approach proved more robust. By processing inputs locally, the framework ensures privacy while offering dependable insights. Overall, the result of this research shows how AI can support preventive healthcare, empowering individuals and clinicians to act early and reduce long-term complications

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