Fig. 1: The Full Elastic-Net Cox model performs similarly to the Random Survival Forest and produces robust mortality risk predictions across both healthy and sick groups, and across groups of differing socio-economic status.

a Selection proportions of each feature as a percentage of the 100 Elastic-Net iterations performed, ranked from highest to lowest. The adopted selection threshold of 80% is indicated in red. b Forest plot comparing the C-Index values (and 95% CI) of (1) a Cox model using sex and age only (null model), (2) the PhenoAge model applied on the Scottish UKBB data, (3) our RSF and (4) our Elastic-Net derived Cox-model (Full ENC). c Comparison of C-Index values of the Full ENC and sex-and-age-only null models for (i) Healthy and Sick groups and (ii) for Lower and Higher rated Townsend Deprivation indexed groups. Across both stratifications, C-Index values of the Full ENC were significantly higher than those produced by the null model, with non-overlapping, or near non-overlapping* confidence intervals, indicating that the Full ENC model provides a statistically significant uplift in predictive ability. The dashed vertical lines represent the C-index values of the Full ENC and the null model on the full Scottish test set. *Whilst the separate confidence intervals of the Full ENC model and the null model visually overlap for the “Healthy” and “Lower TDI” groups, the T-test for the contrast shows a significant (p<5%) difference in C-Index values.