A Hybrid Machine learning Techniques for Detection of Chronic Kidney Disease

Chronic kidney disease (CKD) is a prevalent medical condition that is distinguished by the progressive deterioration of renal function over time. The diagnosis, management, and treatment of this condition are exceedingly difficult due to its multifactorial etiology and extensive array of manifestations. The objective of this review paper is to provide a comprehensive examination of chronic kidney disease, including epidemiology, pathophysiology, clinical presentation, diagnostic criteria, treatment modalities, and preventive strategies. Health professionals must address the intricacies of the condition in order to manage or reduce the complications associated with CKD. Diabetes, hypertension, advanced age, and gender are among the numerous risk factors that increase the likelihood of developing CKD. The predictors of CKD were described in this investigation using machine learning algorithms. Our results indicate that ML can assist in the early identification and management of individuals who are at risk of developing CKD. The application of ML models to electronic health record data can reduce the prevalence of undiagnosed CKD, which is particularly beneficial in progressive diseases such as CKD, where the treatment burden and cost increase as the disease progresses.