Machine Learning Models For Predicting Chronic Kidney Disease

Abstract

Chronic Kidney Disorder is an extreme lifelong
condition that induced via both kidney pathology or reduced
kidney features. Chronic Kidney Disorder affects one in every
five men and one in every four women globally between the ages
of 65 and 74. Early prediction and right treatments can
probably prevent the cease-level. Dialysis or kidney
transplantation is the only way to save patients life. In my study,
I examine how machine-learning can be used to predict chronic
kidney disease (CKD) early. Machine learning models are
effective in CKD prediction. This work proposes a workflow to
predict CKD based on Clinical information’s (clinical data’s).
Data prepossessing, missing value handling method with
filtering and attributes selection are the major processes. Naïve
Bayes Method/Algorithm which has highest Precision, Recall,
Accuracy and F1 score. Also it is less time consuming.