Abstract
WBAN (Wireless Body Area Network) is a cutting-edge technique. The major goal of WBAN is to gather crucial physiological characteristics or signals from patients. In a WBAN, data classification is necessary. Data identification and classification is a tough and time-consuming task. If data is not detected early, it might be fatal. To address this issue, various machine learning methods are used for classification of data in WBAN. The major goal of this review article is to look at several WBAN classification algorithms and suggest one that can manage the variability of data in the healthcare. This publication is crucial in terms of pointing researchers in the right direction when it comes to data classification for healthcare monitoring, as well as an analysis and summary of the most recent work in the field.
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