Disease Prediction Using Machine Learning

Abstract

Big data has a significant part in a number of businesses, but it is largely essential to the rapidly growing healthcare industry. It plays an important role by offering a large set of data points, constructing a robust system which allows for better and more accurate results in disease detection. Originally, the forecasts are made on the information accessible, but the absence of imperfect information contributes to a decrease in the caliber of precision. Besides incomplete data different qualities of particular regional diseases, which change based on their areas of origin can weaken the prediction models further. In this paper we use data mining techniques such as association rule mining, classification, clustering and finally the Decision Tree Machine learning algorithm to analyze the different kinds of general body-based illnesses. We implemented and assessed the efficacy of the Decision Tree algorithm over real-life clinical information.