Paper Title
Implementation of Chronic Kidney Disease Prediction Using Random Forest

Abstract
A major issue, chronic kidney disease has been growing at a constant rate. An individual may only survive for a few days without kidneys which leads to dialysis and kidney trans plantion. The kidneys are harmed in chronic kidney disease (CKD) and aren't able to cleanse blood as they normally do. Heart conditions, anaemia, bone conditions, excessive potassium and calcium levels, and anaemia are among the extremely frequent consequences that arises with kidney failure. The wors case scenario results in total renal failure, necessitating a kidney transplant in order to survive. The quality of life can be increased more by CKD early identification by using machine learning methods like Random forest,Naive Bayes, Decision tree, SVM,KNN. Random forest gives more accuracy than remaining models so we used random forest classifier to predict CKD . Keywords - CKD, Dialysis, Renal failure