Cognitive aging clock based on Explainable artificial intelligence.

ABSTRACT: Biological clocks aim to estimate the physiological state of an organism, or of specific organs or systems, from the perspective of aging. The inferred individual age acceleration is presumably linked to increased mortality and morbidity risks. Here, we introduce a cognitive aging clock based on a battery of five tests (sensomotor arithmetic, sensomotor mirror letter, 1-back, campimetry and the Stroop), capturing sensory and verbal perception, working memory, and decision-making in the context of cognitive conflict. The developed LightGBM machine learning model has the cognitive age MAE of 7.6 years. SHAP analysis yields the overall and individualized interpretations of accelerated aging. In particular, we demonstrate the dominant contribution of Stroop and campimetry test variables to cog

SUBMITTER: Krivonosov MI 

PROVIDER: S-EPMC12722282 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature