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Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia

Author

Listed:
  • Ana Luiza Dallora

    (Department of Health, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden)

  • Leandro Minku

    (School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK)

  • Emilia Mendes

    (Department of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden)

  • Mikael Rennemark

    (Faculty of Health and Life Sciences, Linnaeus University, 351 95 Kalmar, Sweden)

  • Peter Anderberg

    (Department of Health, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden)

  • Johan Sanmartin Berglund

    (Department of Health, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden)

Abstract

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.

Suggested Citation

  • Ana Luiza Dallora & Leandro Minku & Emilia Mendes & Mikael Rennemark & Peter Anderberg & Johan Sanmartin Berglund, 2020. "Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia," IJERPH, MDPI, vol. 17(18), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6674-:d:413097
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    References listed on IDEAS

    as
    1. Ana Luiza Dallora & Shahryar Eivazzadeh & Emilia Mendes & Johan Berglund & Peter Anderberg, 2017. "Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-23, June.
    2. Antonovsky, Aaron, 1993. "The structure and properties of the sense of coherence scale," Social Science & Medicine, Elsevier, vol. 36(6), pages 725-733, March.
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    Cited by:

    1. Raquel Pedrero-Chamizo & Ulrike Albers & Gonzalo Palacios & Klaus Pietrzik & Agustín Meléndez & Marcela González-Gross, 2020. "Health Risk, Functional Markers and Cognitive Status in Institutionalized Older Adults: A Longitudinal Study," IJERPH, MDPI, vol. 17(19), pages 1-13, October.

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