Predicting Diseases via Technology Incorporation

Abstract

Currently, some of the technical innovations that many scholarly investigators have embraced include learning algorithms, machine learning, predictive analytics, and big data analytics. The aim of these innovations has been to aid in useful data extraction for purposes of informed decision-making. Given that the big data section has seen predictive analytics emerge as a promising platform, with machine learning models incorporated, there has been a growing possibility of predicting the future behavior of parameters. In healthcare, this possibility has been felt in terms of disease prediction, as well as cure anticipation and development. The central purpose of this study was to apply a machine learning model on medical data sets in a big data environment. Specifically, the C4.5 algorithm was used towards chronic kidney disease prediction in a healthcare setting marked by big data presence.