EARLY PREDICTION OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING TECHNIQUES: A REVIEW

EARLY PREDICTION OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING TECHNIQUES: A REVIEW

T Suguna , L Sumathi

Abstract

Chronic Kidney Disease (CKD) is a major public health problem,    affecting more than 10% of the world’s population. Early diagnosis and treatment of CKD is essential to reduce the burden of the disease and improve patient outcomes. Machine Learning has been attracting many researchers, and it has been successfully applied in many fields such as banking, e-commerce, and healthcare etc. In recent years, machine learning (ML) techniques have been used to develop predictive models for CKD. This survey paper reviews the current state of the art of ML based CKD prediction, focusing on the most relevant papers published in the last decade. The survey paper discusses the data sets used for training and testing, challenges, and limitations of existing CKD prediction models, and provides recommendations for future research. Additionally, this survey paper provides a comprehensive comparison of the performance of the various machine learning algorithms and techniques used for CKD prediction.

Keywords:

Chronic Kidney Disease, Deep Belief Network, Machine Learning, Early Prediction, SVM