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Objective: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.<\/jats:p>","DOI":"10.3233\/jad-231459","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T14:08:52Z","timestamp":1717510132000},"page":"1-27","source":"Crossref","is-referenced-by-count":19,"title":["Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review"],"prefix":"10.1177","volume":"100","author":[{"given":"Mustafa","family":"Al-Hammadi","sequence":"first","affiliation":[{"name":"School of Information and Engineering, Dalarna University, Falun, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan","family":"Fleyeh","sequence":"additional","affiliation":[{"name":"School of Information and Engineering, Dalarna University, Falun, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Cristina","family":"\u00c5berg","sequence":"additional","affiliation":[{"name":"School of Health and Welfare, Dalarna University, Falun, Sweden"},{"name":"Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kjartan","family":"Halvorsen","sequence":"additional","affiliation":[{"name":"School of Health and Welfare, Dalarna University, Falun, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilias","family":"Thomas","sequence":"additional","affiliation":[{"name":"School of Information and Engineering, Dalarna University, Falun, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-231459_ref1","unstructured":"World Health Organization. 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(2021) Development of Thai picture description task for Alzheimer\u2019s screening using part-of-speech tagging. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2104\u20132109.","DOI":"10.1109\/EMBC46164.2021.9629861"},{"key":"10.3233\/JAD-231459_ref54","doi-asserted-by":"crossref","first-page":"19597","DOI":"10.1038\/s41598-019-56020-x","article-title":"An automatic assessment system for Alzheimer\u2019s disease based on speech using feature sequence generator and recurrent neural network","volume":"9","author":"Chien","year":"2019","journal-title":"Sci Rep"},{"key":"10.3233\/JAD-231459_ref55","doi-asserted-by":"crossref","first-page":"15761","DOI":"10.1007\/s00521-018-3494-1","article-title":"On the analysis of speech and disfluencies for automatic detection of mild cognitive impairment","volume":"32","author":"L\u00f3pez-de-Ipi\u00f1a","year":"2020","journal-title":"Neural Comput 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Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pp. 358\u2013365."},{"key":"10.3233\/JAD-231459_ref79","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2174\/1567205014666171120143800","article-title":"Advances on automatic speech analysis for early detection of Alzheimer disease: A non-linear multi-task approach","volume":"15","author":"L\u00f3pez-de-Ipi\u00f1a","year":"2018","journal-title":"Curr Alzheimer Res"},{"key":"10.3233\/JAD-231459_ref80","unstructured":"Praat: Doing phonetics by computer. , https:\/\/www.fon.hum.uva.nl\/praat\/. 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