Using Machine Learning Methods to Predict Cognitive Age from Psychophysiological Tests.

ABSTRACT: Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed 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. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, R2, and CV_R2 metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected throu

SUBMITTER: Tyurina DD 

PROVIDER: S-EPMC12733081 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature