  Cognition in midlife is an important predictor of age-related mental decline
and statistical models that predict cognitive performance can be useful for
predicting decline. However, existing models struggle to capture complex
relationships between physical, sociodemographic, psychological and mental
health factors that effect cognition. Using data from an observational, cohort
study, Midlife in the United States (MIDUS), we modeled a large number of
variables to predict executive function and episodic memory measures. We used
cross-sectional and longitudinal outcomes with varying sparsity, or amount of
missing data. Deep neural network (DNN) models consistently ranked highest in
all of the cognitive performance prediction tasks, as assessed with root mean
squared error (RMSE) on out-of-sample data. RMSE differences between DNN and
other model types were statistically significant (T(8) = -3.70; p < 0.05). The
interaction effect between model type and sparsity was significant (F(9)=59.20;
p < 0.01), indicating the success of DNNs can partly be attributed to their
robustness and ability to model hierarchical relationships between
health-related factors. Our findings underscore the potential of neural
networks to model clinical datasets and allow better understanding of factors
that lead to cognitive decline.
