THE TAXONOMY: HEALTH MONITORING SYSTEM USING MACHINE LEARNING TECHNIQUES

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Y. Vineetha; K.Krishna Kishore

THE TAXONOMY: HEALTH MONITORING SYSTEM USING MACHINE LEARNING TECHNIQUES

Authors: Y. Vineetha, K.Krishna Kishore

Unique Paper ID: 149206
Volume: 6
Issue: 11
PageNo: 301-306

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

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Cite This Article

ISSN: 2349-6002
Volume: 6
Issue: 11
PageNo: 301-306

THE TAXONOMY: HEALTH MONITORING SYSTEM USING MACHINE LEARNING TECHNIQUES

Available:https://ijirt.org/article?manuscript=149206

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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