title: An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.
abstract: In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision,G-mean,F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Huiling
authors.identifier: 0000-0002-7714-9693
authors.initials: H
authors.lastname: Chen
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Hui
authors.identifier: 0000-0002-9119-954X
authors.initials: H
authors.lastname: Huang
authors.affiliation: School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
authors.forename: Xuehua
authors.initials: X
authors.lastname: Zhao
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: ZhenNao
authors.initials: Z
authors.lastname: Cai
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Changfei
authors.initials: C
authors.lastname: Tong
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
authors.affiliation: Cancer Hospital, Chinese Academy of Medical Sciences and Shenzhen Hospital, Shenzhen 518000, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D010300:Parkinson Disease / Q000175:diagnosis; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results