Investigating predictors of cognitive decline using machine learning The Journals of Gerontology: Series B Oxford Academic

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.