Predictive Model for Smart Healthcare Systems Using Random Forest Classifier

Health care systems in developing countries often face serious challenges, including limited resources, poor infrastructure, and delays in patient care. This study presents the development of a predictive model designed to assist in early health risk detection, particularly in resource-constrained settings. In this study, an improved predictive model for smart healthcare systems using Random Forest Classifier was created and embedded in a simple web interface. The model was trained on synthetic medical data and achieved an accuracy of 91% during testing. Health workers and others were able to use the system effectively, even with minimal digital skills. The platform provided real-time predictions, that will help users make quicker clinical decisions.

Smart health care, Predictive model, Random Forest Classifier, Machine Learning, Decision Support System, Real-time Prediction

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