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Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions
Aging Research Center, Karolinska Institutet, Solna, Stockholm, Sweden ; Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
Department of Computer Science, University of Science and Technology Bannu, Township, Khyber-Pakhtunkhwa, Bannu, Pakistan.
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2023 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 47, no 1, article id 17Article in journal (Refereed) Published
Abstract [en]

Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations. 

Place, publisher, year, edition, pages
Springer Nature Switzerland AG , 2023. Vol. 47, no 1, article id 17
Keywords [en]
Artificial Intelligence, Dementia, Humans, Machine Learning, Voice, human, Deep learning, Dementia prediction, Feature selection
National Category
Computer Sciences Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Family-Centred Health
Identifiers
URN: urn:nbn:se:his:diva-22246DOI: 10.1007/s10916-023-01906-7ISI: 000920053700001PubMedID: 36720727Scopus ID: 2-s2.0-85147143895OAI: oai:DiVA.org:his-22246DiVA, id: diva2:1735569
Note

CC BY 4.0

Attribution 4.0 International (CC BY 4.0)

© 2023, The Author(s). email: johan.sanmartin.berglund@bth.se

© 2023 Springer Nature Switzerland AG. Part of Springer Nature.

Published: 01 February 2023

Open access funding provided by Blekinge Institute of Technology. This research received no external funding.

Corrigendum in: Journal of Medical Systems, Volume 48, September 2024, Article number 87. doi:10.1007/s10916-024-02109-4

The author name “Liaqata Ali” in the original publication of this article was incorrect. The correct name is “Liaqat Ali”

Available from: 2023-02-09 Created: 2023-02-09 Last updated: 2024-09-26Bibliographically approved

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Publisher's full textPubMedScopusCorrection to: Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions (Journal of Medical Systems, (2023), 47, 1, (17), 10.1007/s10916-023-01906-7)

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Anderberg, Peter

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