A Comprehensive Analysis to Detect Chronic Kidney Disease and Stage Prediction: Using Machine Learning

Chronic Kidney Disease (CKD) is worldwide health concern that causes other diseases and has a high rate of morbidity and mortality. CKD often progresses without noticeable symptoms in its early stages. Early detection is critical to delay or prevent progression to kidney failure. Traditional detection relies on lab tests (e.g., serum creatinine, GFR, urinalysis). Machine learning and Deep learning model can complement these methods by identifying subtle patterns in clinical or imaging data. This article investigates the applications of ensemble learning methods, such as AdaBoost, Random Forest, Gradient Boosting, and Voting Classifiers, for CKD prediction. These models address significant problems with CKD datasets, including missing values, unbalanced classes, and excessive complexity, by utilizing the benefits of several base learners. The ensemble learning models provide a tool for the early detection of CKD and tailored treatment by effectively representing the different patterns and interactions observed in the data.