Detection of Chronic Disease using Machine Learning Techniques

Amruta H Raut , Tejashree H Jadhav , Yogita G Borole , Dr M A Pradhan

Machine Learning; Chronic Disease; Naive Bayes Algorithm; Decision Tree

According to World Health Organization (WHO), chronic diseases are the leading cause of death worldwide. With the improvement of living standards, the incidences of chronic disease is increasing. Hence it is important to perform risk assessments for chronic diseases. Since there is enormous amount of data available from medical industry, it could be useful for discovering hidden patterns and developing a classifier using existing machine learning techniques. Accurate analysis of medical data benefits early disease detection and patient care which in turn benefits the society. Preprocessing of data plays a significant role for enhancing accuracy of classification systems. In this paper, we use machine learning algorithms for effective prediction of chronic disease. We first did the comparative study of different machine learning algorithm then we propose a new framework based on two machine learning algorithms: Naive Bayes Algorithm and Decision Tree Algorithm. We apply both these algorithms on the dataset and predict the outcome. We also compare the results obtained by applying these algorithms to conclude which of the algorithm is better and more accurate.

Volume 4 Issue 4, April-2019

Pages : 110 - 113