|Title||Investigating predictors of cognitive decline using machine learning.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Casanova, R, Saldana, S, Lutz, MW, Plassman, BL, Kuchibhatla, M, Hayden, KM|
|Journal||Journals of Gerontology, Series B: Psychological Sciences & Social Sciences|
|Keywords||Alzheimer's disease, Cognitive Ability, Machine learning|
Objectives: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer's disease(AD), to predict cognitive decline.
Methods: Health and Retirement Study participants, aged 65-90, with DNA and >2 cognitive evaluations, were included (n=7,142). Predictors included age, body mass index, gender, education, APOE ε4, CVD, hypertension, diabetes, stroke, neighborhood socio-economic status(NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity and specificity) were reported.
Results: Three classes were identified. Discriminating highest from lowest classes produced the best RF performance: accuracy=78%(1.0%), sensitivity=75%(1.0%) and specificity=81%(1.0%). Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status and BMI. When discriminating high from medium classes, top predictors were education, age, gender, stroke, diabetes, NSES and BMI. When discriminating medium from the low classes, education, NSES, age, diabetes and stroke were top predictors.
Discussion: The combination of latent trajectories and RF classification techniques suggested that non-genetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination.
|User Guide Notes|
|Alternate Journal||J Gerontol B Psychol Sci Soc Sci|
|PubMed Central ID||PMC7205421|
|Grant List||P30 AG049638 / AG / NIA NIH HHS / United States |
R01 AG042633 / AG / NIA NIH HHS / United States