Automated Sensing of Chronic Kidney Disease Using SVM and Random Forest Algorithm

Chronic kidney disease is a rising health problem and involves a condition that decrease the efficiency of renal functions and that damages the kidney. Chronic kidney disease may be detected with several automated diagnosis system, and these have been classified using various features and classifier combinations. In this project, SVM and Random forest classifiers is proposed for the diagnosis of chronic kidney disease. The classification performances are estimated with different performance metrics. The use of SVM and Random forest integrated network enhanced the classification accuracy of the model. The proposed model successfully classified the samples with a better accuracy.