Disease Prediction Efficacy on Community Healthcare and Population Lived Experiences

Abstract

Currently, trends in infectious disease occurrences, incidence, and prevalence are unknown in the majority of cases, complicating efforts geared towards disease prediction. In this study, the motivation was to contribute to infectious disease prediction through deep learning algorithms’ parameters’ optimization, with big data in the healthcare industry on consideration. Specifically, there was a comparison between the performances of the long-short term memory (LSTM) learning algorithm and deep neural network model with the performance of ARIMA (autoregressive integrated moving average) algorithm. Three infectious diseases were examined to discern model reliability and validity in under different experimental conditions. From the findings, this study established that the performance of LSTM and DNN frameworks is superior to that of ARIMA. Relative to chickenpox prediction, there was improvement by 19% and 25% after implementing LSTM and top-10 DNN models, respectively.