Using machine learning methods to predict cognitive age from psychophysiological tests
This addresses cognitive aging diagnosis and monitoring for healthcare applications, though it appears incremental as it applies existing ML methods to new psychophysiological data.
This study developed a machine learning method to predict cognitive age from psychophysiological test data, achieving predictions based on parameters like reaction time and memory scores, which enables remote screening for cognitive aging via mobile devices.
This study introduces a novel method for predicting cognitive age using psychophysiological tests. To determine cognitive age, subjects were asked to complete a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. Based on the tests completed, the average completion time, proportion of correct answers, average absolute delta of the color campimetry test, number of guessed words in the Münsterberg matrix, and other parameters were calculated for each subject. The obtained characteristics of the subjects were preprocessed and used to train a machine learning algorithm implementing a regression task for predicting a person's cognitive age. These findings contribute to the field of remote screening using mobile devices for human health for diagnosing and monitoring cognitive aging.