Predicting cognitive impairment using novel functional features of spatial proximity and circularity in the digital clock drawing test

The digital clock drawing test (dCDT) is a cognitive screening tool employing a digital pen. 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, and compare them to commonly used summary features. We included dCDTs from 3,415 participants from the Framingham Heart Study. Random forest models with five-fold cross-validation were trained to distinguish participants with mild cognitive impairment or dementia from cognitively intact participants. When combined with established time-based features, functional features related to spatial proximity and circularity demonstrated predictive power comparable to commonly used summary 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.