RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction. Issue 9 (26th May 2022)

• Record Type: Journal Article
• Authors: Theerthagiri, Prasannavenkatesan; Ruby, A. Usha
• Other Names: Herrero Álvaro guestEditor.; Urda Daniel guestEditor.; Sedano Javier guestEditor.; Quintián Héctor guestEditor.; Corchado Emilio guestEditor.; Ahmed Syed Hassan guestEditor.; Khan Murad guestEditor.; Guibene Wael guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-26
• Subjects: chronic kidney disease -- decision tree classifiers -- machine learning -- random forest ensemble -- recursive feature selection; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13048
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Chronic kidney disease is a global health issue that affects millions of people worldwide and causes significant social, economic, and medical issues. Several automated detection systems can diagnose chronic kidney disease. This paper proposes the recursive random forest feature selection (RFFS) based ensemble learning algorithm to diagnose chronic kidney diseases (CKD). In the decision point, decision tree‐based classifiers are used. The accuracy and kappa scores are used to determine the classification results. According to the results of the proposed algorithm's performance analyses, the ensemble learning classifiers outperform other classifiers for classifying CKD. The proposed RFFS algorithm achieves 6%–40% improved prediction accuracy using the feature selection algorithm. Further, it attains 15%–39% of reduced mean square error. The performance metrics, precision, sensitivity, specificity, f1 score, and Jaccard scores, have been analysed and show greater results for the RFFS algorithm. Thus, the proposed RFE‐GB algorithm results prove it as a prominent model for CKD estimation and treatment.