An Ensemble Prediction Model for Chronic Kidney Disease Using Optimizedfeature Selection

Electronic Health Records have enabled the biosciences to advance more significantly and produce vast amounts of data. Due to this massive amount of data, there is an essential necessity for knowledge development. In this area of biosciences, machine learning has a significant impact. The inability of the kidney to carry out its normal functions, such as excretion and regulating the body’s electrolyte balance, is known as Chronic Kidney Disease (CKD), otherwise called chronic kidney failure. CKD may be brought on by an ancestral record of renal disease, excessive blood pressure or diabetes. The likelihood of this kidney dysfunction deteriorating further over time is substantial. Heart conditions, anaemia, bone conditions, excessive potassium and calcium levels, and anaemia are among the extremely frequent consequences that arise with kidney failure. To predict CKD, a solid prediction algorithm is needed. This study predicts CKD using data preprocessing, Particle Swarm optimization (PSO) based feature selection and a variety of classifiers including ensemble classifiers applied on the chronic kidney disease dataset obtained from the UCI Repository. It provides an optimum prediction model for detection of CKD. The outcomes of the framework indicate positive signs of improved CKD prediction.