Chronic disease prediction using effective feature selection

Abstract

Healthcare is a major sector where there is demand for predictive analytics using machine learning. Healthcare will be largely benefited when useful knowledge can be transferred into timely action to manage hazardous situations in medical sector. Chronic kidney disease is a life threatening disease which can be prevented with timely right predictions and appropriate precautionary measures. In this paper, various machine learning classifiers are applied on the medical dataset to develop a prediction model to tell if a person's present medical condition can lead to the chronic stage of the disease in future. The higher prediction accuracy and decreased build time is obtained with reduced feature set attributes by applying Best First and Greedy stepwise algorithm combined with different classification techniques like Naive Bayes,Support vector machine (SVM), J48, Random Forest, and K Nearest Neighbor(KNN).