A systematic assessment and meta-analysis of machine learning methods for predicting and Classifying severe Long Term kidney disease

Heart failure (HF), atherosclerotic cardiovascular disease, and end-stage kidney disease are all signs of long term kidney disease brought on by type II diabetes (CKD). However, because it has no symptoms, CKD is frequently overlooked or misdiagnosed. Without kidneys, a human beings can able live for about 18 days, necessitating dialysis and a kidney transplant. It is complex to have accurate tools for early diagnosis of kidney diseases. Additionally, patients frequently forego the common urine protein-based CKD detection test. It describes a medical condition that damages the kidneys and has an impact on the body as a whole. Inadequate diagnosis and care can lead to end-stage renal disease, which eventually kills the patient. In response to the shortcomings of conventional biomarkers and the requirement for early therapeutic intervention in cats with CKD to improve outcomes, new renal biomarkers for the detection of glomerular or tubular dysfunction have been discovered and validated. Changes in the concentrations of these biomarkers in the blood or urine may reveal early kidney damage or forecast the development of kidney disease before changes in conventional biomarkers can be seen. On Utilization of machine learning (ML) approaches, CKD prediction is simple. The method of the present study for predicting long term kidney disease and classifying it on basis of the clinical diagnosis information includes data preprocessing, a method for handling missing data, feature reduction and feature extraction. We examine some recent works on ML-based CKD detection and classification in this systematic review. It analyze of the step of data processing from data collection to data classification on utilizing the dataset preprocessing step, hybrid or ensemble feature extraction technique, meta-heuristics based feature selection technique and finally detection and classification of the kidney disease. To be more precise, we group this systematic review into two important types: supervised learning, and unsupervised learning based CKD detection and classification. Peer-reviewed papers were gathered using reputable search engines like Elsevier, IEEE Xplore, PubMed, Scopus, Web of Science, and others. Finally, we briefly discuss current problems and the requirements for linking academic research on CKD detection and classification to industry-based solutions that will improve standard clinical care.