Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN)

ABSTRACT

COVID-19 endemic has made the entire world face an extraordinary challenging situation which has made life in this world a fearsome halt and demanding numerous lives. As it has spread across 212 nations and territories and the infected cases and deaths are increased to 5,212,172 and 334,915 (as of May 22 2020). Still, it is a real hazard to human health. Severe Acute Respiratory Syndrome cause vast negative impacts economy and health populations. Professionals involved in COVID test can commit mistakes when testing for identifying the disease. Evaluating and diagnosing the disease by medical experts are the significant key factor. Technologies like machine learning and data mining helps substantially to increase the accuracy of identifying COVID. Artificial Neural Networks (ANN) has been extensively used for diagnosis. Proposed Single Hidden Layer Feedforward Neural Networks (SLFN)-COVID approach is used to detect COVID-19 for disease detection on creating the social impacts and its used for treatment. The experimental results of the proposed method outperforms well when compared to existing methods which achieves 83% of accuracy, 73% of precision, 68% of Recall, 82% of F1-Score. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.