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Multifactorial 10-year prior diagnosis prediction model of dementia
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0002-6752-017X
University of Birmingham, GBR .
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-0449-5322
Linnaeus University, SWE.
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2020 (English)In: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, Vol. 17, no 18, p. 1-18, article id 6674Article in journal (Refereed) Published
Abstract [en]

Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI AG , 2020. Vol. 17, no 18, p. 1-18, article id 6674
Keywords [en]
Cost sensitive learning, Decision tree, Dementia, Machine learning, Modifiable risk factors, Prognosis, Wrapper feature selection, health risk, mental disorder, modeling, prediction, risk factor, symptom, Sweden
National Category
Geriatrics Neurology
Identifiers
URN: urn:nbn:se:bth-20482DOI: 10.3390/ijerph17186674ISI: 000579987200001PubMedID: 32937765Scopus ID: 2-s2.0-85090858921OAI: oai:DiVA.org:bth-20482DiVA, id: diva2:1470717
Funder
Swedish Research Council
Note

open access

Available from: 2020-09-25 Created: 2020-09-25 Last updated: 2023-12-04Bibliographically approved

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Multifactorial 10-year prior diagnosis prediction model of dementia(756 kB)227 downloads
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Moraes, Ana Luiza DalloraMendes, EmiliaAnderberg, PeterSanmartin Berglund, Johan

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Moraes, Ana Luiza DalloraMendes, EmiliaAnderberg, PeterSanmartin Berglund, Johan
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Department of HealthDepartment of Software EngineeringDepartment of Computer Science
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International Journal of Environmental Research and Public Health
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