دورية أكاديمية
A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer's disease.
| العنوان: | A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer's disease. |
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| المؤلفون: | Wang L; School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 215 6th Street, Hangzhou, Zhejiang 310018, China.; Hangzhou Vocational & Technical College, 68 Xueyuan Street, Hangzhou, Zhejiang 310018, China., Sheng J; School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 215 6th Street, Hangzhou, Zhejiang 310018, China., Zhang Q; Beijing Hospital, 1 Dahua Road, Beijing 100730, China.; National Center of Gerontology, 1 Dahua Road, Beijing 100730, China.; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 1 Dahua Road, Beijing 100730, China., Yang Z; School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 215 6th Street, Hangzhou, Zhejiang 310018, China., Xin Y; School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 215 6th Street, Hangzhou, Zhejiang 310018, China., Song Y; Beijing Hospital, 1 Dahua Road, Beijing 100730, China.; National Center of Gerontology, 1 Dahua Road, Beijing 100730, China.; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 1 Dahua Road, Beijing 100730, China., Zhang Q; School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 215 6th Street, Hangzhou, Zhejiang 310018, China., Wang B; School of Computer Science and Technology, Hangzhou Dianzi University, 1158 2nd Street, Hangzhou, Zhejiang 310018, China.; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 215 6th Street, Hangzhou, Zhejiang 310018, China. |
| المصدر: | Cerebral cortex (New York, N.Y. : 1991) [Cereb Cortex] 2024 Aug 01; Vol. 34 (8). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 9110718 Publication Model: Print Cited Medium: Internet ISSN: 1460-2199 (Electronic) Linking ISSN: 10473211 NLM ISO Abbreviation: Cereb Cortex Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York, NY : Oxford University Press, c1991- |
| مواضيع طبية MeSH: | Alzheimer Disease*/genetics , Alzheimer Disease*/diagnostic imaging , Support Vector Machine* , Magnetic Resonance Imaging*/methods , Algorithms*, Humans ; Brain/diagnostic imaging ; Imaging Genomics/methods ; Neuroimaging/methods ; Cognitive Dysfunction/diagnostic imaging ; Cognitive Dysfunction/genetics ; Male ; Aged ; Female |
| مستخلص: | In recent years, brain imaging genomics has advanced significantly in revealing underlying pathological mechanisms of Alzheimer's disease (AD) and providing early diagnosis. In this paper, we present a framework for diagnosing AD that integrates magnetic resonance imaging (fMRI) genetic preprocessing, feature selection, and a support vector machine (SVM) model. In particular, a novel sand cat swarm optimization (SCSO) algorithm, named SS-SCSO, which integrates the spiral search strategy and alert mechanism from the sparrow search algorithm, is proposed to optimize the SVM parameters. The optimization efficacy of the SS-SCSO algorithm is evaluated using CEC2017 benchmark functions, with results compared with other metaheuristic algorithms (MAs). The proposed SS-SCSO-SVM framework has been effectively employed to classify different stages of cognitive impairment in Alzheimer's Disease using imaging genetic datasets from the Alzheimer's Disease Neuroimaging Initiative. It has demonstrated excellent classification accuracies for four typical cases, including AD, early mild cognitive impairment, late mild cognitive impairment, and healthy control. Furthermore, experiment results indicate that the SS-SCSO-SVM algorithm has a stronger exploration capability for diagnosing AD compared to other well-established MAs and machine learning techniques. (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.) |
| معلومات مُعتمدة: | 62271177 National Natural Science Foundations of China; LZ24F010007 Key Program of the Natural Science Foundation of Zhejiang Province; LTGY23F020004 Natural Science Foundation of Zhejiang Province |
| فهرسة مساهمة: | Keywords: Alzheimer’s disease; imaging genetic; optimization; sand cat swarm algorithm; support vector machine |
| تواريخ الأحداث: | Date Created: 20240815 Date Completed: 20240815 Latest Revision: 20240815 |
| رمز التحديث: | 20240816 |
| DOI: | 10.1093/cercor/bhae329 |
| PMID: | 39147391 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1460-2199 |
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| DOI: | 10.1093/cercor/bhae329 |