A Data-Driven Preventive Healthcare System for Multi-Disease Risk Prediction Using Machine Learning

The presented work developed an intelligent healthcare prediction platform designed for early multi-disease risk assessment A systematic comparison of multiple classification-based  algorithms was conducted to identify the best performing predictive approach for each disease domain. The experimental evaluation confirmed that the tree-based ensemble model consistently achieved superior predictive accuracy and delivered dependable results across both disease datasets. The trained models were successfully embedded within a web-based healthcare application, enabling real-time disease risk generation in a clinically accessible format. The system helps doctors and hospital staff perform faster disease screening and make better decisions about early treatment and prevention. Additional features like patient record storage, report generation, and visual charts make the tool more useful in day-to-day hospital operations. Prospective extensions of this work may encompass the incorporation of prediction modules for additional disease categories, expansion of the training datasets to enhance model generalizability, experimentation with deep learning architectures for improved accuracy, and the integration of advanced monitoring dashboards and visualization tools to better serve real-world clinical application requirements.

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