A Comprehensive Analysis of Machine Learning Algorithms in Diagnosis of Chronic Kidney Disease

Abstract

Chronic Kidney Disease (CKD) is a major health condition that causes millions of deaths and billions of dollars in worldwide economic loss. The classical way of diagnosis and treatment of CKDs is through routine blood tests, urine analysis and ultrasonography of kidneys. Though all of these methods are very effective in diagnosis of CKDs, they lack predictive power. Artificial Intelligence and Machine Learning models are more feasible options to drill in the clinical data and predict the occurrence of CKD from the vital body parameters. This article reviews the major works in the diagnosis of CKDs using Data Mining (DM) and Machine Learning (ML) techniques. The significant challenges and limitations in the data driven predictive techniques are also discussed. Finally, the future research directions that would improve the predictive power of the models are also highlighted.