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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\u2019s Disease using imaging genetic datasets from the Alzheimer\u2019s 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.<\/jats:p>","DOI":"10.1093\/cercor\/bhae329","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T12:42:14Z","timestamp":1721911334000},"source":"Crossref","is-referenced-by-count":6,"title":["A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer\u2019s disease"],"prefix":"10.1093","volume":"34","author":[{"given":"Luyun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University , 1158 2nd Street, Hangzhou, Zhejiang 310018 , China"},{"name":"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"},{"name":"Hangzhou Vocational & Technical College , 68 Xueyuan Street, Hangzhou, Zhejiang 310018 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7662-9126","authenticated-orcid":false,"given":"Jinhua","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University , 1158 2nd Street, Hangzhou, Zhejiang 310018 , China"},{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Hospital , 1 Dahua Road, Beijing 100730 , China"},{"name":"National Center of Gerontology , 1 Dahua Road, Beijing 100730 , China"},{"name":"Institute of Geriatric Medicine , Chinese Academy of Medical 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Song","sequence":"additional","affiliation":[{"name":"Beijing Hospital , 1 Dahua Road, Beijing 100730 , China"},{"name":"National Center of Gerontology , 1 Dahua Road, Beijing 100730 , China"},{"name":"Institute of Geriatric Medicine , Chinese Academy of Medical Sciences, 1 Dahua Road, Beijing 100730 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University , 1158 2nd Street, Hangzhou, Zhejiang 310018 , China"},{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbing","family":"Wang","sequence":"additional","affiliation":[{"name":"School 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