INVESTIGATION ANALYSIS ON E-HEALTHCARE MONITORING SYSTEM WITH IOT AND BIG DATA BY TSLR-CHSC MODEL

Abstract

Internet of Things (IoT) technology helped the development of E-Health care Monitoring System from direct visit to virtual Monitoring (Telemedicine). Smart health care system in IoT environment monitored the basic health signs such as heartbeat rate, body temperature and blood pressure in real-time applications. IoT and big data is a prominent challenge in smart healthcare system. In this health monitoring system big data is employed to analyze the large volume of data and to determine the Normal and Abnormal patient condition. Numerous issues like accuracy, time and error have yet to be conveyed to generate a ductile system for health care monitoring. To address these issues, I proposed a method called Theil Sen Linear Regression and Canopy Hopkins Statistics Clustering (TSLR-CHSC) for IoT based Health monitoring system is proposed. This method splits into three sections such as Data collection, Feature Selection and Clustering. First, Cardiovascular disease dataset is acquired from sensors are collected. Second, appropriate features can be selected by using Theil Sen regression feature. Third, clustering is performed based on the cluster tendency by using canopy algorithms. Through this way I deployed an efficient E-Health Monitoring System with minimum time consumption. For evaluation, a cardiovascular disease dataset is obtained from various medical sensor devices are analyzed to identify the disease severity. Keyword: IoT, Big Data, Theil Sen Linear Regression, Canopy Hopkins Statistic Clustering, Health Care Monitoring.