THE TAXONOMY: HEALTH MONITORING SYSTEM USING MACHINE LEARNING TECHNIQUES
Author(s):
Y. Vineetha, K.Krishna Kishore
Keywords:
WBAN, Intensive care unit, SVM,K-prototypes
Abstract
In this paper patient health monitoring system using the machine learning techniques are studied. wireless body area networks (WBAN) is a network which provides continous monitoring over or inside the human body for the grate time and it holds the transmission real time traffic such as the information, sound, video to observe the status of the basic organs functions. For the soldiers health status we use some bio-sensor systems which comprise various types of the small physiological sensors. In this the ability to track the location and monitor health of the soldiers in real time who become lost and injured in the battlefield. For the soldiers health monitoring k-means clustering algorithms are used. K-means clustering algorithm is the one of the method of the machine learning. By using the k-means clustering algorithms the collected data will be uploaded on the cloud for the data analysis and the predictions. In this two new systems that deal with the large amount of the data and solve the main problems in the current monitoring system. In the current monitoring system in ICU has the many issues to detect real states of patients namely critical and normal states. It frequently generates the high number of the false alarms having the bad effects on their working conditions. These false alarms can threat the patient life and misleading the medical staff. To avoid this problem support vector machines (SVM) namely LASVM and ISVM techniques with the k- prototype clustering methods
Article Details
Unique Paper ID: 149206

Publication Volume & Issue: Volume 6, Issue 11

Page(s): 301 - 306
Article Preview & Download


Go To Issue



Call For Paper

Volume 7 Issue 1

Last Date 25 June 2020


About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:+91 820 061 5067
Email: editor@ijirt.org
Website: ijirt.org

Policies