Detection of chronic kidney disease using binary whale optimization algorithm

Abstract

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.