Predictive Model for Smart Healthcare Systems Using Random Forest Classifier
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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.
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