An Effective IoT‐ML‐Based Approach for Patient Monitoring and Disease Prediction in Smart Healthcare
TL;DR
An IoT‐ML‐based health‐care framework that collects physiological signals, preprocesses the signals, and implements optimization based on ML algorithms to reveal potential health risks and can facilitate early medical interventions, hospital visit reduction, and personalized, proactive management of healthcare is described.
Abstract
The convergence of Internet of Things (IoT) and Machine Learning (ML) technologies allows for a new paradigm in smart healthcare, with real‐time, ubiquitous patient monitoring, as well as the ability to predict diseases early in order to facilitate preventative measures. Based on this, the work presented here describes an IoT‐ML‐based health‐care framework that collects physiological signals: heart rate, ECG, BP, oxygen level using wearable sensors, preprocesses the signals, and implements optimization based on ML algorithms (SVM, RF, KNN, LR) to reveal potential health risks. This method implemented data cleaning, organization, and fuzzy feature extraction, thus ensuring high‐quality input to achieve a precise classification of a disease. The sample evaluation conducted for this framework indicated superior predictive power for the accuracy of the suggested model 99.50%, sensitivity of 99.34%, F1‐score of 99.46%, and specificity of 99.45%. The efficiency of the suggested method indicates it can facilitate early medical interventions, hospital visit reduction, and personalized, proactive management of healthcare.