Abstract
A timely critical condition detection and early notification are two essential requirements in a healthcare wireless body area network for the correct treatment of patients. However, most of the systems have limited capabilities and so could not detect the exact condition in a precise time interval. In addition to these it needs a reduction in the false alert rate, as issuing alerts for the deviation in each incoming packet increases the false alert rate and these false alerts consume more network resources. In order to fulfill the above-mentioned requirements, a dynamic alert system has been designed in this regard to make it more efficient, also, a new kind of hybridization approach is being introduced to it with the additive support of a nature-inspired optimization strategy named Lion Hunting and a machine-learning technique called support vector machine. The simulation is done using a network simulator NS-2.35, and the proposed alerting system outperforms others.
Highlights
Developing of a better alerting system for the correct diagnosis of patient’s condition plays a very imperative role in Healthcare Wireless Body Area Network (HWBAN), as false diagnosis leads incorrect decisions, which further causes problems in treatment and even became fatal for patients’ life in some cases (Ando et al, 2016; Hauskrecht et al, 2013)
In order to fulfill the above-mentioned requirements, a dynamic alert system has been designed in this regard to make it more efficient, a new kind of hybridization approach is being introduced to it with the additive support of a nature-inspired optimization strategy named Lion Hunting and a machinelearning technique called support vector machine
In life-critical HWBANs, it is always preferable to transmit accurate data within a specific time interval, as even a slight ignorance in diagnosis can make a significant difference in the decision
Summary
Developing of a better alerting system for the correct diagnosis of patient’s condition plays a very imperative role in Healthcare Wireless Body Area Network (HWBAN), as false diagnosis leads incorrect decisions, which further causes problems in treatment and even became fatal for patients’ life in some cases (Ando et al, 2016; Hauskrecht et al, 2013). Received data may be imprecise due to various reasons like a sensor or link error, limited resources, interference during transmission, and transmission error, etc All of these issues may lead to generate false alerts (Haque, et al, 2015; Dickson, & Thomas, 2015). The main objective of the LH-SVMAS is to lessen the false alert rate and provide the accurate classification of the critical packets so that they can be transmitted as early as possible with no loss towards the concerned caregiver. Unlike other protocols, it does not require user intervention considered as much more effective than others and the simulation outcomes proof the same in a better way.
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