Chronic Kidney Disease Prediction Using Robust Approach in Machine Learning

The degenerative condition known as Chronic Kidney Disease (CKD) impairs kidney function and causes waste products to build up in the body. Early CKD prediction and identification can stop or slow the disease's progression. This study uses demographic, clinical, and lab data to provide a machine learning-based method for CKD prediction. The suggested approach uses a mix of feature selection and classification algorithms to pinpoint important risk factors and provide very accurate predictions for CKD. Effective therapies to stop or delay the course of the illness depend on the early detection and prediction of CKD. This work utilizes demographic, clinical, and laboratory data to present a unique machine learning-based method for CKD prediction and achieving an accuracy of 89%. There is a lot of promise in developing adaptive, automated, and intelligent diagnostic methods for CKD prediction. These algorithms offer individualized forecasts, adapt to unique patient profiles, and continually learn from fresh data. These systems can improve accuracy and help healthcare professionals make knowledgeable decisions about the diagnosis and treatment of CKD by integrating feature selection, dimensionality reduction, and classification algorithms.