In this article, we present two novel aging clocks created within the deep learning paradigm – PsychoAge and SubjAge
DISCUSSION
Both these clocks use the same set of 50 psychosocial features to estimate human chronological age and subjective age, respectively. These clocks showed superior performance during CV in MIDUS 1 (MAEPsychoAge= 6.70 years; MAESubjAge= 7.32 years) and were verified in two other large data sets – MIDUS 2 and MIDUS Refresher ( Table 1 ). In terms of epsilon accuracy, PsychoAge reached a score of 0.78 in MIDUS 1, and SubjAge – 0.74.
Having trained and verified the final models, we aimed to understand how PsychoAge and SubjAge see human aging and what features they pay the most attention to. With a tandem PFI-DFS approach we ranked all features according to their relative importance. Top-5 important features in both clocks were associated with health conditions (e.g. headache frequency) and relationship status (marital status, expectations from sex life in 10 years). Less significant features greatly differ in their relative importance for SubjAge and PsychoAge predictions. For example, top-20 PsychoAge features contain only one personality trait – neuroticism. Meanwhile, the only personality traits encountered among top-20 SubjAge features are – extraversion and openness.
These three personality traits, along with conscientiousness and agreeableness form the big five traits, which are commonly used in practice and scientific research to describe the human mental state landscape. Read more
