Efficient Clustering Simulator for Hierarchical Management of High-Risk with Wellness

Abstract

In recent years, various research fields, such as Information Technology (IT), Nano Technology (NT), Bio Technology (BT), Culture Technology (CT), Space Technology (ST), and Environment Technology (ET), have fused to solve a large number of issues in the real world. In particular, service solutions have emerged by combining IT and BT in the area of wellness research. In addition, major studies have conducted on wellness. These include implanting medical technology into the human body, sensor recognition technology that detects various changes in the human body, sensor miniaturization technology that attaches sensors to the human body, and high-risk group monitoring technology that uses various pieces of sensor data from the human body. In particular, the high-risk group has many different types of diseases, such as depression, suicide, diabetes, high blood pressure, and cancer, which be divided into mental and physical illnesses. It is highly important to manage patients in the high-risk group hierarchically because of the critical nature of their diseases. Therefore, although technological fusion has achieved a wireless body area network (WBAN), it has mainly concentrated on measurement via sensors, coverage, and communication range. Therefore, this paper proposes a High-risk Hierarchical Clustering Simulator (H2CS) for the hierarchical management of patients in high-risk groups. The H2CS provides hierarchical management functions by actively recording high-risk level, based on the grades and distances of high-risks.