MOSurvivor-Guided Joint CpG Selection and XGBoost Hyperparameter Optimization for Compact Epigenetic Age Prediction

Accurate epigenetic age prediction from DNA methylation profiles is intrinsically high-dimensional, creating a need for parsimonious models that preserve predictive performance while reducing the number of assayed cytosine-phosphate-guanine (CpG) loci. This study introduces MOSurvivor, a population-based multi-objective search framework that jointly optimizes a weight-threshold CpG selector and eight XGBoost hyperparameters. Experiments used the GSE40279 whole-blood cohort (656 individuals profiled on the Illumina HumanMethylation450 platform). After retaining 1,000 age-correlated CpGs, five strategies were evaluated on the same 30 seeded 80:20 train/test splits: fixed-parameter XGBoost using all 1,000 CpGs, random search, a genetic algorithm, particle swarm optimization, and MOSurvivor. Internal fitness was estimated using three-fold cross-validation on each training set. Across the 30 held-out test sets, MOSurvivor achieved a mean absolute error (MAE) of 4.149 {+/-} 0.300 years, root mean squared error of 5.545 {+/-} 0.392 years, and R2 of 0.855{+/-} 0.027 while retaining 211.6 {+/-} 54.8 CpGs. Relative to full-feature XGBoost (MAE 4.095 {+/-} 0.285 years), MOSurvivor reduced the feature set by 78.8% at an MAE increase of only 0.054 years (1.3%). Paired Wilcoxon tests found no significant accuracy difference between MOSurvivor and any comparator (all unadjusted p > 0.05; all Holm-adjusted p [≥] 0.476). The most recurrent locus, cg16867657, appeared in 29 runs, whereas mean pairwise Jaccard similarity was 0.124, indicating a small stable core embedded in multiple near-equivalent feature subsets. MOSurvivor thus offers a competitive accuracy-parsimony trade-off rather than superior absolute accuracy. External validation and leakage-free nested feature preselection remain necessary before biological or clinical translation. Keywords: epigenetic clock, DNA methylation, feature selection, multi-objective optimization, XGBoost, metaheuristics, biological aging.