[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119850-en":3,"doc-seo-119850-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119850,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Alzheimer's Disease Diagnosis Using Machine Learning: A Survey","Alzheimer’s disease is a neurodegenerative disorder that affects central nervous system function and impairs cognitive processing, causing gradual decline in mental abilities. Growing research efforts focus on identifying Alzheimer’s early and slowing disease progression. Evidence highlights contributions from genetic factors, stress, and nutrition. Machine-learning and AI-based computer-aided analysis can improve the study of neuroimaging methods and non-imaging biomarkers. This survey compares 80+ publications from 2017 onward, covering core learning architectures and additional design approaches, and summarizes key challenges and future directions.","applied sciences  \nArticle  \nAlzheimer's Disease Diagnosis Using Machine Learning: A Survey  \nOmer Asghar Dara 1, Jose Manuel Lopez-Guede 1, *, Hasan Issa Raheem 1, Javad Rahebi 2, Ekaitz Zulueta 1 and Unai Fernandez-Gamiz 3  \nCitation: Dara, O.A.; Lopez-Guede, J.M.; Raheem, H.I.; Rahebi, J.;  \nZulueta, E.; Fernandez-Gamiz, U. Alzheimer's Disease Diagnosis Using Machine Learning: A Survey. Appl.  \nSci. 2023, 13, 8298. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app13148298](10.3390/app13148298)  \nAcademic Editors: Mitsutaka Nemoto and Katsuhiro Mikami  \nReceived: 10 June 2023  \nRevised: 13 July 2023  \nAccepted: 16 July 2023  \nPublished: 18 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain;  \n[serkirde.dara@gmail.com](serkirde.dara@gmail.com) (O.A.D.); [h_alyaqoobi@yahoo.com](h_alyaqoobi@yahoo.com) (H.I.R.); [ekaitz.zulueta@ehu.eus](ekaitz.zulueta@ehu.eus) (E.Z.)  \n2 Department of Software Engineering, Istanbul Topkapi University, 34087 Istanbul, Turkey; [cevatrahebi@topkapi.edu.tr](cevatrahebi@topkapi.edu.tr)  \n3 Department of Nuclear Engineering and Fluid Mechanics, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain; [unai.fernandez@ehu.eus](unai.fernandez@ehu.eus)  \n* [Correspondence: jm.lopez@ehu.eus](Correspondence: jm.lopez@ehu.eus)  \nAbstract: Alzheimer's is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual's cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer's disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and nonimage biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017 . Alzheimer's disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer's disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer's disease.  \nKeywords: Alzheimer's disease diagnosis; machine learning; feature selection  \n1. Introduction  \nAlzheimer's disease is a prevalent contributor to cognitive decline in the elderly population. Mild cognitive impairment (MCI) exhibits an annual progression rate to Alzheimer's disease (AD) ranging from 10% to 15%[1] . Mild cognitive impairment (MCI) can be understood as a transitional phase between the cognitive decline associated with dementia and the cognitive abilities t","cbCaitd0jilwqY2d","https://ap.wps.com/l/cbCaitd0jilwqY2d","pdf",1885045,1,24,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Mild cognitive impairment (MCI) and progression\n## Need for timely diagnosis\n## Computer-aided detection via machine learning\n# Diagnosis and evaluation approaches","[{\"question\":\"What does the survey focus on regarding Alzheimer’s disease detection?\",\"answer\":\"It reviews and compares research publications on Alzheimer’s diagnosis using machine learning and AI-based computer-aided analysis, including neuroimaging methods and non-image biomarkers.\"},{\"question\":\"Which machine learning models are mentioned as commonly used for Alzheimer’s detection?\",\"answer\":\"The text highlights fundamental architectures such as support vector machines, decision trees, and ensemble models.\"},{\"question\":\"Why is timely and accurate diagnosis important according to the document?\",\"answer\":\"The document states that timely and accurate medical diagnosis can inhibit disease progression, and that there is currently no definitive medical diagnosis or treatment available.\"}]","Alzheimer's Disease Diagnosis Using Machine Learning: A Survey | 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