IoT-Driven Healthcare an Ensemble Learning Approach for Early Detection and Prevention of Chronic Diseases

Abstract

This investigate presents an IoT-driven healthcare system leveraging outfit learning methods for early discovery and avoidance of unremitting illnesses. By joining information from wearable sensors, shrewd domestic gadgets, and electronic wellbeing records, the system offers a comprehensive wellbeing checking framework. Gathering learning calculations, counting Random Forest, Slope Boosting, Stacking, and Bolster Vector Machines, are utilized to combine numerous models' qualities, improving forecast exactness and robustness. Experimental comes about on an assorted dataset illustrate the viability of the proposed approach, accomplishing a precision of 85%, accuracy of 87%, review of 84%, F1-score of 85%, and AUC-ROC of 0.92. Comparative examinations with standard calculations and related work within the field emphasize the prevalence of the outfit learning approach in leveraging IoT information for healthcare applications. This investigation contributes to progressing IoT-driven healthcare by giving bits of knowledge into the potential of gathering learning procedures for illness expectation and avoidance. By making strides early location and personalized mediations, the system points to upgraded understanding results, diminishing healthcare costs, and progress in populace well-being management.