PREDICTIVE MACHINE LEARNING (ML) ALGORITHM USING IOT FRAMEWORK FOR NOVEL CORONA VIRUS (COVID-19)

Abstract

During earlier months of the pandemic COVID-19 with no recommended cure or vaccine available only solution to destroy the chain is self-isolation which can be maintained by physical distancing. This is now understood that the world require much faster solution to accommodate and deal with the future COVID-19 spread over the world by non-clinical methods namely data mining, augmented intelligence and several Artificial Intelligence (AI) techniques. It has become a huge hindrance to mitigate for the healthcare industry to provide more potential involved for patient's diagnosis and also for effective prognosis of 2019-CoV pandemic. Therefore, the proposed framework is implemented with the Internet of Things (IoTs) in healthcare industry for collecting the symptom data of real-time that is beneficial in predicting whether the person gets infected with COVID-19 virus or not. This can be done through various signs namely body temperature, blood oxygen level, headache, coughing patterns, etc. Thus, the research work focused on faster identification of COVID-19 virus infection cases potentially using Machine Learning (ML) algorithm from the real-time symptom data. Moreover, the obtained results have illustrated that K-Nearest Neighbour (KNN) algorithm is highly efficient while compared with other ML algorithms such as Naive Bayes and Logistic Regression (LR) in predicting the possible recovery of the infected patients from pandemic COVID-19 with the accuracy of 96.85%.