Approximation of Cognitive Performance Using an Elastic Net Regression Model Trained on Gait, Visual, Auditory, Postural, and Olfactory Function Features

Abstract

When dementia is diagnosed, it is most often already past the point of irreversible neuronal deterioration. Neuropsychological tests are frequently used in clinical settings; however, they must be administered properly and are oftentimes conducted after cognitive impairment becomes apparent or is raised as a concern by the patient or a family member. It would be beneficial to develop a non-invasive system for approximating cognitive scores which can be utilized by a general practitioner without the need for cognitive testing. To this end, gait, visual, auditory, postural, and olfactory function parameters, reported history of illness, and personal habits were used to train an elastic-net regression model in predicting the cognitive score. Community-dwelling men (N = 104) above the age of sixty-five participated in the current study. Both individual variables and principal components of the motor and sensory functions were included in the elastic-net regression model, which was trained on 70% of the dataset. The years of education, limits of stability testing time, regular ophthalmological exams, postural testing time principal component, better ear score on the sentence recognition test, and olfactory discrimination score largely contributed to explaining over 40% of the variance in the cognitive score.