Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Shubiao Wu; Peng Mao; Rizeng Li; Zhennao Cai; Ali Asghar Heidari; Jianfu Xia; Huiling Chen; Majdi Mafarja; Hamza Turabieh; Xiaowei Chen

doi:10.1016/j.compbiomed.2021.104582

Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Comput Biol Med. 2021 Aug:135:104582. doi: 10.1016/j.compbiomed.2021.104582. Epub 2021 Jun 17.

Authors

Shubiao Wu¹, Peng Mao², Rizeng Li³, Zhennao Cai⁴, Ali Asghar Heidari⁵, Jianfu Xia⁶, Huiling Chen⁷, Majdi Mafarja⁸, Hamza Turabieh⁹, Xiaowei Chen¹⁰

Affiliations

¹ Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China. Electronic address: 194511981393@stu.wzu.edu.cn.
² Department of Pain Medicine, China-Japan Friendship Hospital, Beijing, 100029, China. Electronic address: doctormaopeng@126.com.
³ Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University, Wenzhou Central Hospital, Wenzhou, Zhejiang, 325000, China. Electronic address: 13857761117@163.com.
⁴ Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China. Electronic address: cznao@wzu.edu.cn.
⁵ Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore. Electronic address: aliasgha@comp.nus.edu.sg.
⁶ Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University, Wenzhou Central Hospital, Wenzhou, Zhejiang, 325000, China. Electronic address: xia189687@163.com.
⁷ Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China. Electronic address: chenhuiling.jlu@gmail.com.
⁸ Department of Computer Science, Birzeit University, POBox 14, West Bank, Palestine. Electronic address: mmafarja@birzeit.edu.
⁹ Department of Information Technology, College of Computers and Information Technology, P.O. Box11099, Taif, 21944, Taif University, Taif, Saudi Arabia. Electronic address: h.turabieh@tu.edu.sa.
¹⁰ Department of Rheumatology and Immunology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. Electronic address: 179781679@qq.com.

PMID: 34214940
DOI: 10.1016/j.compbiomed.2021.104582

Abstract

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.

Keywords: Fuzzy K-Nearest neighbors; Linear population size reduction mechanism; Lupus nephritis; Optimization; Sine cosine algorithm.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Benchmarking
Cluster Analysis
Fuzzy Logic
Humans
Lupus Nephritis*