Using machine learning methods to predict cognitive age from psychophysiological tests

TL;DR

This paper presents a machine learning approach to predict cognitive age from psychophysiological test data, enabling remote cognitive health monitoring and aging assessment.

Contribution

It introduces a novel machine learning method that predicts cognitive age based on diverse psychophysiological test parameters, advancing remote cognitive health screening.

Findings

Successful prediction of cognitive age using psychophysiological data

Potential for remote cognitive health monitoring

Enhanced understanding of cognitive aging markers

Abstract

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 M\"unsterberg 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.