• V.Deepa
• Dr.K.Rajeswari

New possibilities for E-health care monitoring are provoked by the expansion of Internet of Things (IoT) and Big data, in addition to the pervasive nature of small wearable sensors. The IoT and big data is a paramount issue in numerous domain areas including e-healthcare systems due to its importance. Big data are notably utilized in e-healthcare to ascertain the normal and abnormal patient condition. Numerous research works were introduced for e-healthcare monitoring by many researchers varying from diagnosis to disease recognition and prevention on effective e-healthcare monitoring. 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, in this work, a method called Theil Sen Linear Regression and Canopy Hopkins Statistic Clustering (TSLR-CHSC) for IoT-based healthcare monitoring is proposed. The TSLR-CHSC method is split into three sections, namely, data collection, feature selection and clustering. First, big data comprising of cardiovascular disease dataset acquired from sensors are collected. Next, relevant features with maximum accuracy and minimum time are obtained using the Theil–Sen Estimated Linear Regression Feature Selection model. Followed by which with the relevant features, clustering is performed by means of Canopy Hopkins Statistic Clustering for healthcare monitoring. Here, with the aid of Canopy Clustering determining the cluster tendency to what degree clusters exist in data to be clustered. By this way, an efficient diseased patient health monitoring is carried out with minimal time consumption. For experimentation, a systematic cardiovascular healthcare data is produced utilizing kaggle dataset and medicinal gadgets to foresee the diverse patient levels of disease severity. A detailed comparative analysis is carried out and the simulation outcome ensured the goodness of the TSLR-CHSC method over the compared methods under various aspects.

Internet of Things, Big Data, Theil–Sen, Linear Regression, Feature Selection, Canopy Hopkins, Statistic Clustering, healthcare monitoring

"THEIL SEN REGRESSION AND CANOPY HOPKINS STATISTIC CLUSTERING FOR E-HEALTHCARE MONITORING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 11, page no.c123-c136, November-2021, Available :http://www.jetir.org/papers/JETIR2111216.pdf

"THEIL SEN REGRESSION AND CANOPY HOPKINS STATISTIC CLUSTERING FOR E-HEALTHCARE MONITORING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 11, page no. ppc123-c136, November-2021, Available at : http://www.jetir.org/papers/JETIR2111216.pdf