Research Article | Open Access
DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS
Mr. M.SUDHAKAR, MOHAMMED ZUBAIR AHMED, GANGASANI THARUN, SAJJAN ROHITH, JARUPULA MAHENDER
Pages: 667-672
Abstract
The development and exploitation of several prominent Data mining techniques in numerous realworld
application areas (e.g. Industry, Healthcare and Bio science) has led to the utilization of such
techniques in machine learning environments, in order to extract useful pieces of information of the
specified data in healthcare communities, biomedical fields etc. The accurate analysis of medical
database benefits in early disease prediction, patient care and community services. The techniques of
machine learning have been successfully employed in assorted applications including Disease
prediction. The aim of developing classifier system using machine learning algorithms is to
immensely help to solve the health-related issues by assisting the physicians to predict and diagnose
diseases at an early stage. A Sample data of 4920 patients’ records diagnosed with 41 diseases was
selected for analysis. A dependent variable was composed of 41 diseases. 95 of 132 independent
variables (symptoms) closely related to diseases were selected and optimized. This research work
carried out demonstrates the disease prediction system developed using Machine learning algorithms
such as Decision Tree classifier, Random forest classifier, and Naïve Bayes classifier. The paper
presents the comparative study of the results of the above algorithms used.
Keywords
The development and exploitation of several prominent Data mining techniques in numerous realworld