Implementation of Health Monitoring System for Patients using Machine Learning Algorithms

Abstract

To enhance monitoring and forecasting skills, we investigate in this research study the inclusion of cutting-edge technology in the industrial and healthcare domains. We created a machinelearning-based solution for the wellness program industry that uses Internet Of Medical Things (IoMT) devices to forecast cardiovascular risk. Our model outperformed previous approaches in diagnosing cardiovascular disease (CVD) with higher accuracy, recall, and F1-score. It did this by using a fuzzy logic classifier for illness prediction and a random forest for feature selection. Additionally, to enhance overall equipment effectiveness (OEE), lower electricity costs, and decrease unplanned downtime in manufacturing settings, we created a real-time system leveraging smart systems and machine learning. During testing on a manufacturing blender, this device tracked operational phases and load-balancing conditions well. We employed the Decision Tree Algorithm to train and assess a model that produced a perfection of 66.66%.