Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data

TL;DR

This study demonstrates that deep neural networks outperform traditional models in predicting cognitive performance from complex, sparse, and heterogeneous data, highlighting their robustness and hierarchical modeling capabilities.

Contribution

The paper introduces the application of deep neural networks to predict cognitive performance using large, sparse datasets, showing improved accuracy over existing models.

Findings

DNN models achieved the lowest RMSE in prediction tasks.

DNN performance was significantly better than other models (p < 0.05).

DNNs effectively modeled hierarchical relationships in health data.

Abstract

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.