Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures.

In last decades, chronic kidney disease (CKD) becomes a global health problem that is steadily developing worldwide. It is a chronic illness highly related to increased morbidity and mortality, cardiovascular diseases, and high healthcare cost. Earlier identification and classification of CKD is treated as a major factor in controlling the mortality rate. Data mining (DM) techniques are used for the extraction of hidden details from the clinical and laboratory patient data that is used to aid doctors in enhancing diagnostic accuracy. Recently, machine learning (ML) techniques are commonly employed for the prediction and classification of diseases in healthcare sector. With this motivation, this study examines the performance of different ML algorithms to diagnose CKD at the earlier stages. The proposed model involves data pre-processing in two stages such as missing value replacement and data transformation. Besides, a set of five ML based classification models are involved such as support vector machine (SVM), random forest (RF), logistic regression (LR), K-nearest neighbor (KNN), and decision tree (DT). For investigating the performance of the different ML models, a benchmark CKD dataset from UCI repository is employed and the results are examined under different aspects. Among the different classifiers, the RF model has accomplished superior results with the maximum precision of 0.99, recall of 0.99, and F-score of 0.99 with a minimal error rate of 0.012.