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Multifactorial 10-y...
Multifactorial 10-year prior diagnosis prediction model of dementia
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- Moraes, Ana Luiza Dallora (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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- Minku, Leandro (författare)
- School of Computer Science, University of Birmingham, United Kingdom,Univ Birmingham, UK,University of Birmingham, GBR
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- Mendes, Emilia (författare)
- Blekinge Tekniska Högskola,Institutionen för programvaruteknik,Institutionen för datavetenskap
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- Rennemark, Mikael, 1951- (författare)
- Linnéuniversitetet,Institutionen för psykologi (PSY),Faculty of Health and Life Sciences, Linnaeus University, Kalmar, Sweden,Linnaeus University, SWE
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- Anderberg, Peter (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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- Sanmartin Berglund, Johan, Professor (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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(creator_code:org_t)
- 2020-09-14
- 2020
- Engelska.
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:18, s. 1-18
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Geriatrik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Geriatrics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
- SAMHÄLLSVETENSKAP -- Psykologi (hsv//swe)
- SOCIAL SCIENCES -- Psychology (hsv//eng)
Nyckelord
- 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
- Psychology
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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