Investigating predictors of cognitive decline using machine learning.

TitleInvestigating predictors of cognitive decline using machine learning.
Publication TypeJournal Article
Year of Publication2020
AuthorsCasanova, R, Saldana, S, Lutz, MW, Plassman, BL, Kuchibhatla, M, Hayden, KM
JournalJournals of Gerontology, Series B: Psychological Sciences & Social Sciences
ISSN Number1758-5368
KeywordsAlzheimer'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 JournalJ Gerontol B Psychol Sci Soc Sci
Citation Key9581
PubMed ID29718387
PubMed Central IDPMC7205421
Grant ListP30 AG049638 / AG / NIA NIH HHS / United States
R01 AG042633 / AG / NIA NIH HHS / United States