Health Care Surveillance Using Machine Learning and Data Analytics

Abstract

Any country's healthy population are a true asset. Both developed and developing nations are spending enormous sums of money to strengthen their "healthcare systems" and the required "health infrastructure." Globally, only few countries have a proactive approach to healthcare. The pandemic that has been going on recently has taught countries hard lessons about how important it is to have strong healthcare systems. The formulation of effective health policies and initiatives depends heavily on studies on Public Health Surveillance (PHS) and the conclusions that follow. This task has become clearer with the introduction of modern computing techniques and technology. Humanity has always been saved by technology when it is applied correctly. In this context, the most promising machine learning techniques are applied in this work. An empirical investigation of RBFN classifiers is presented in this paper. A variety of performance criteria, including recall, f-score, accuracy, precision, and False Positive Rate (FPR), are used to evaluate the efficacy of the recommended procedures. The RBFN approach has the highest accuracy and the least amount of time complexity in identifying health diseases.