ABSTRACT
COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.