Classifiers voting based Decision Support System for Prediction of Kidney Related Chronic Diseases

Abstract

Chronic kidney diseases are increasing exponentially due to hypertension, diabetes, anemia and other related factors. Patients with such diseases usually remain unaware of initial symptoms leading to difficulties in diagnosis of the disease. High performance data mining-based diagnosis and prediction techniques could assist the patient in self-analysis and medical practitioners in developing a precise opinion about patient. This research presents a framework for clinical decision support system of chronic kidney disease (CKD) on the basis of knowledge and facts provided by specialists and experts. To diagnose the disease and decide about progression stage of CKD, different classification algorithms are applied and evaluated on the dataset. The proposed methodology increases the accuracy to 91.75 % and reduces the cost of predicting the stages of CKD using LMT algorithms on the dataset.