title: Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.
abstract: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.
  
  
  
  The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC).
  
  
  
  AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype.
  
  
  
  The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.
authors: Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.
authors: Timo
authors: T
authors: Pekkala
authors: Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.
authors: Anette
authors: A
authors: Hall
authors: VTT Technical Research Centre of Finland, Tampere, Finland.|Combinostics, Tampere, Finland.
authors: Jyrki
authors: J
authors: Lötjönen
authors: Combinostics, Tampere, Finland.
authors: Jussi
authors: J
authors: Mattila
authors: Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.|Department of Neurology, Kuopio University Hospital, Kuopio, Finland.
authors: Hilkka
authors: H
authors: Soininen
authors: Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.
authors: Tiia
authors: T
authors: Ngandu
authors: Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.|Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.|Hospital District of North Karelia, Joensuu, Finland.
authors: Tiina
authors: T
authors: Laatikainen
authors: Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.|Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.|Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden.
authors: Miia
authors: M
authors: Kivipelto
authors: Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.|Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden.
authors: Alina
authors: A
authors: Solomon
mesh_terms: D000368:Aged; D001057:Apolipoproteins E / Q000235:genetics; D002561:Cerebrovascular Disorders / Q000453:epidemiology; D003071:Cognition / Q000502:physiology; D018672:Community Health Planning; D003704:Dementia / Q000175:diagnosis* / Q000453:epidemiology* / Q000235:genetics; D005260:Female; D005387:Finland / Q000453:epidemiology; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D012720:Severity of Illness Index; D000069553:Supervised Machine Learning
chemical_list: D001057:Apolipoproteins E
keywords: Computer-assisted decision making; dementia; prediction; prevention; supervised machine learning