Predictive Health Monitoring System for Patient Risk Assessment using Machine Learning

Cardiovascular diseases continue to be among the top causes of death globally, therefore stressing the importance of early and correct detection. Conventional models for predicting heart disease tend to be based on IoT monitoring systems, with limitations like high expense, network reliance, and delay in real-time processing. To overcome these shortcomings, this paper introduces a Machine Learning (ML) approach that does away with IoT reliance while improving prediction performance. The system recommended combining sophisticated data preprocessing and feature selection methods to deliver interpretable predictions. Real-time alerts also facilitate early intervention for patients at risk. Comparative evaluation against current models illustrates enhanced accuracy based on precision, recall, and general predictability, which makes the strategy a cost-saving and effective option for early heart disease diagnosis.