Predicting cognitive impairment using novel functional features of spatial proximity and circularity

Music City Center 

The digital clock drawing test (dCDT) screens for cognitive impairment using a digital pen to track movements as participants draw a clock from memory. While many studies rely on summary statistics of dCDT features to predict cognitive outcomes, these approaches often involve subjective decisions such as feature selection and imputation. In this study, we introduce novel dCDT features, expressed as mathematical functions, to capture more granular aspects of the test. We compare the performance of these functions against traditional summary features, assessing their ability to offer deeper insights into cognition. These features account for the circularity of the clock, spatial proximity of drawing points, and pressure applied to the paper. When combined with established time-based features, functional features related to spatial proximity and circularity demonstrated predictive power comparable to commonly used features. Our findings highlight the potential of integrating functional features to detect subtle motions and behaviors in digital cognitive assessments, offering new tools that may enhance diagnostic accuracy and support early detection strategies.

dementia

digital clock drawing test

functional data analysis

predictive modeling

machine learning 

Biometrics Section