Systematic Survey on Chronic Kidney Disease Prediction Using Different Machine Learning Techniques

Abstract

Chronic kidney disease (CKD), a condition characterized by gradual loss of kidney function, poses a global health challenge, affecting a substantial population worldwide. Attributable to renal pathology or sustained kidney damage, CKD serves as a harbinger of morbidity and mortality. The diagnosis of this condition remains a formidable task, fraught with risks, high costs, and extensive duration, often leading to late detection in resource-constrained settings. Recent advancements have introduced an array of improved algorithms that have enhanced the efficiency of risk assessment for CKD. This survey provides a comprehensive examination of various machine learning (ML) algorithms employed in the prognostication of CKD. It is posited that the deployment of machine learning technologies could revolutionize diagnostic methodologies, transitioning to a machine-assisted strategy. The efficacy of diverse algorithms has been systematically evaluated using multiple criteria. Through this analysis, the most effective classifiers for predicting CKD have been identified, with the potential to significantly refine clinical practices. Ultimately, this study delineates the ML algorithms that hold promise for the future of CKD diagnosis and treatment, contributing to the advancement of medical informatics in nephrology.