Research Paper Volume 13, Issue 20 pp 23471—23516

Predicting physiological aging rates from a range of quantitative traits using machine learning


Figure 1. Predictive performance of machine learning models. (A) Comparison of the predictive performance measured by the coefficient of determination (R2) and mean absolute error (MAE) for all machine learning models investigated in the study. The random forest classifier (RFC) model was among the top-performing models. (B) Physiological aging rates were highly correlated between different models. Shown is a tileplot of R2 between PARs obtained from different models where darker green corresponds to higher values. (C) Physiological ages predicted by the RFC model were well-correlated with chronological ages of individuals in the SardiNIA study. (D) Physiological aging rate (PAR) of individuals obtained from the RFC model was weakly correlated with chronological age. All figures shown are for the baseline (W1) SardiNIA study. Similar results as in (C) and (D) were observed in follow-up waves of the SardiNIA study, for the elastic net regression model, and in the InCHIANTI study (see Supplementary Materials).