Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia

Ana Luiza Dallora, Leandro Minku, Emilia Mendes, Mikael Rennemark, Peter Anderberg and Johan Sanmartin Berglund
Additional contact information
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

IJERPH, 2020, vol. 17, issue 18, 1-18

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.

Keywords: dementia; prognosis; modifiable risk factors; decision tree; cost sensitive learning; wrapper feature selection; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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