[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118903-en":3,"doc-seo-118903-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118903,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Application of machine learning in dementia diagnosis - A systematic literature review","According to the World Health Organization, dementia affects over 55 million people globally and generates around 10 million new cases each year, making early diagnosis crucial for planning and management. This study reviews the current relevance of machine learning for dementia prediction through a 20-year literature search in the Scopus database. After bibliometric and content analysis, 202 studies were selected and 25 were analyzed, highlighting continued growth and remaining open research questions. It examines diagnostic targets, used imaging and demographic features, common datasets, and frequently applied algorithms and combinations.","Heliyon 9 (2023) e21626  \nContents lists available at ScienceDirect  \nHeliyon  \n[journal homepage: www.cell.com/heliyon](journal homepage: www.cell.com/heliyon)  \n| Systematic review and meta-analysis\u003Cbr>Application of machine learning in dementia diagnosis: A systematic literature review |  |  |  |\n| --- | --- | --- | --- |\n| Gauhar Kantayeva ∗ , José Lima, Ana I. Pereira\u003Cbr>Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politecnico de Bragança, Bragança, Portugal |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| MSC: 0000\u003Cbr>1111\u003Cbr>Keywords:\u003Cbr>Machine learning Dementia\u003Cbr>Alzheimer’s disease Neurodegenerative diseases |  | According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer’s disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results. |  |\n\n1. Introduction  \nDementia is a decline in cognition that obstructs daily, domestic, or social functioning in the last decades of life [1]. Studies have shown that risk factors associated with numerous diseases and injuries in the past cause dementia. In contrast, it is attainable to cut back the risk factors through an active lifestyle, avoiding harmful habits, such as smoking and alcohol consumption, healthy nutrition, and weight and blood sugar control.  \n* Corresponding author.  \nE-mail address: [gauharka1996@gmail.com](gauharka1996@gmail.com) (G. Kantayeva).  \n[https://doi.org/10.1016/j.heliyon.2023.e21626](https://doi.org/10.1016/j.heliyon.2023.e21626)  \nReceived 3 September 2022; Received in revised form 9 October 2023; Accepted 25 October 2023  \nAvailable online 4 November 2023  \n2405-8440/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nG. Kantayeva, J. Lima and A.I. Pereira Heliyon 9 (2023) e21626  \nFig. 1. The widely used machine learning algorithms.  \nBeing diagnosed early and receiving an accurate rate of","cbCaiiCbHY4ppv6K","https://ap.wps.com/l/cbCaiiCbHY4ppv6K","pdf",1929232,1,13,"English","en",105,"# Introduction\n## Dementia background and risk factors\n## Importance of early and accurate diagnosis\n## Clinical limitations and diagnostic workflow\n## Mild Cognitive Impairment (MCI)\n## Prevalence and geographic outlook","[{\"question\":\"Why is early diagnosis of dementia important?\",\"answer\":\"Early diagnosis and accurate estimation of disease progression support better quality of life and help patients plan future decisions, while enabling appropriate treatment and care.\"},{\"question\":\"How does the study conduct its literature review?\",\"answer\":\"The review combines bibliometric and content analysis for articles published over 20 years in the Scopus database, starting from 27,520 papers and narrowing to 202 selected and 25 analyzed studies.\"},{\"question\":\"What features and algorithms are most commonly used for machine learning approaches in dementia diagnosis?\",\"answer\":\"Most studies rely on magnetic resonance imaging (MRI) and related types, often alongside demographic and cognitive measures such as age, gender, and MMSE scores, using datasets such as ADNI. Algorithms frequently include SVM, ensemble methods, and CNN, with many studies combining multiple machine-learning techniques.\"}]","Application of machine learning in dementia diagnosis - A systematic literature review | PDF",1785720879,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"application-of-machine-learning-in-dementia-diagnosis-a-systematic-literature-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-in-dementia-diagnosis-a-systematic-literature-review/118903/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early diagnosis of dementia important?","Question",{"text":76,"@type":77},"Early diagnosis and accurate estimation of disease progression support better quality of life and help patients plan future decisions, while enabling appropriate treatment and care.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study conduct its literature review?",{"text":81,"@type":77},"The review combines bibliometric and content analysis for articles published over 20 years in the Scopus database, starting from 27,520 papers and narrowing to 202 selected and 25 analyzed studies.",{"name":83,"@type":74,"acceptedAnswer":84},"What features and algorithms are most commonly used for machine learning approaches in dementia diagnosis?",{"text":85,"@type":77},"Most studies rely on magnetic resonance imaging (MRI) and related types, often alongside demographic and cognitive measures such as age, gender, and MMSE scores, using datasets such as ADNI. 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