Review on Diagnosis of Chronic Kidney Disease using Machine Learning

Early diagnosis and characterization are the important components in determining the treatmentofchronic kidney disease (CKD)CKD isanailment which tends to damage the kidneyand affect their effective fuctioning ofexcreting waste and balancing body fluids.Some of the complications included arehypertension, anemia (low bloodcount),mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications.Earlyanderrorfree detectionof CKD can be helpful inaverting further deterioration of patient‘s health. These chronic diseases are prognosticated using various types of data mining classification approaches andmachine learning (ML) algorithms. This Prediction is performed usingRandomForest (RF) Classifier, Logistic Regression (LR) and K-Nearest Neighbor (K-NN) algorithm and Support Vector Machine (SVM) The data used is collected from the UCI Repository with 400 data sets with 25 attributes. This data has been fed into Classification algorithms. The experimentalresults show that KNN, LR, SVM handsout an accuracy of 94%, 98% and 93.75%respectively. TheRF classifier gives out a maximum accuracy of 100%