Maximize Throughput of Emergency Data in WBAN Using Machine Learning

Wireless Body Area Network (WBAN) has sparked a lot of attention because of its self-automation and superior technology. Many researchers have worked in this field. The most difficult challenge for WBAN is to handle diverse traffic in a network with limited resources. Because of their crucial nature, some sensed data are regarded as more significant than others in the healthcare WBAN. Such kind of vital information must be given within a specific time frame. In these systems, data delivery with loss and delay may not be accepted; hence, the use of an intelligent algorithm to copse with these systems is required. As a result, this technology may be used in medical critical circumstances to diagnose and treat patients promptly. To improve the classification accuracy, random forest (RF) and support vector machine (SVM) classifiers are used; these are machine learning techniques. This approach is in charge of determining the class of each incoming packet and assigning a priority to it.