A Performance Comparison Prediction of Chronic Kidney Diseases Using Different Machine Learning Algorithms

In recent years, there is a massive rate of morbidity and mortality due to CKD (chronic kidney disease) which do not have any symptoms in early stages. There are different causes of kidney disease like diabetes, blood pressure, history of kidney disease in family, obesity, etc. Symptoms can be observed like swelling of legs, frequent urination, feeling weak and sick, etc. Various supervised classification algorithms are used for predicting chronic kidney disease. In this study, a comparative analysis is done between different classification algorithms like SVM, random forest, decision tree, etc., using different cleaning methods of missing values.