Research Article Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification Anwer Mustafa Hilal , 1 Areej A. Malibari, 2 Marwa Obayya, 3 Jaber S. Alzahrani, 4 Mohammad Alamgeer, 5 Abdullah Mohamed, 6 Abdelwahed Motwakel, 1 Ishfaq Yaseen, 1 Manar Ahmed Hamza , 1 and Abu Sarwar Zamani 1 1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia 2 Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 3 Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia 4 Department of Industrial Engineering, College of Engineering Alqunfudah, Umm Al-Qura University, Mecca, Saudi Arabia 5 Department of Information Systems, College of Science & Art Mahayil, King Khalid University, Abha, Saudi Arabia 6 Research Centre, Future University, Egypt, New Cairo 11845, Egypt Correspondence should be addressed to Anwer Mustafa Hilal; a.hilal@psau.edu.sa Received 9 March 2022; Revised 20 April 2022; Accepted 27 April 2022; Published 14 May 2022 Academic Editor: Laxmi Lydia Copyright © 2022 Anwer Mustafa Hilal et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. e latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. e major aim of the FSS- OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). e application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene ex- pression classification outcomes. e experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. e proposed FSS-OANFIS model has resulted in a maximum classi- fication accuracy of 89.47%. 1. Introduction Microarray is an advanced technology that helps to recognize the pattern of gene expression of various genes at a time at the genomic level. It supports the researcher to investigate and analyze millions of genes in a single experiment [1]. It identifies many present diseases con- nected to each individual gene such as anaemia and cancer. Analysis of Gene Expression provides a method to recognize the gene that is differentially expressed [2], which is accountable to develop some diseases. Also, it shows the difference between normal and abnormal genes through a mathematical model [3, 4]. Many openly ac- cessible datasets such as Array Express and Gene Ex- pression Omnibus (GEO) make the task easier to identify gene patterns of rare diseases. Classification of gene Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 1698137, 12 pages https://doi.org/10.1155/2022/1698137