Performance Comparison of Machine Learning Algorithms for Classification of Chronic Kidney Disease (CKD)

Abstract

Kidney is one of the vital organs in a human body while ironically, chronic kidney disease (CKD) is one of the main causes of death in the world. Due to the low rate of loss of kidney function, the disease is often overlooked until it is in a really bad condition. Dysfunctional kidney may lead to accumulation of wastes in blood which would affect several other systems and functions of the body such as blood pressure, red blood cell production, vitamin D and bone health. Machine learning algorithms can help in classifying the patients who have CKD or not. Even though several studies have been made to classify CKD on patients using machine-learning tool, not many researchers perform pre-processing and feature selection technique to obtain quality and dependable result. Machine learning used with feature selection techniques are shown to have better and more dependable result. In this study, feature selection methods such as Random Forest feature selection, forward selection, forward exhaustive selection, backward selection and backward exhaustive selection were identified and evaluated. Then, machine learning classifiers such as Random Forest, Linear and Radial SVM, Naïve Bayes and Logistic Regression were implemented. Lastly, the performance of each machine-learning model was evaluated in terms of accuracy, sensitivity, specificity and AUC score. The results showed that Random Forest classifier with Random Forest feature selection is the most suitable machine learning model for classification of CKD as it has the highest accuracy, sensitivity, specificity and AUC with 98.825%, 98.04%, 100% and 98.9% respectively which outperformed other classifiers.