<p>S2 Table; Figs S1 and S2; Tables S2-S11.</p>

Across all conditions, CHIT achieves near-perfect or perfect downstream classification, with SVC reaching up to 100% accuracy on the CKD dataset under MNAR, and 98.75% under MCAR and MAR. Competing methods degrade by up to 19.38 percentage points under MNAR, while CHIT’s accuracy remains stable. Two structural properties account for this resilience: an iterative enrichment of the regression training set as records are completed, and a within-record prioritisation of the most data-scarce features for model-based filling.

Taken together, these results provide mechanism-specific guidance for imputation selection in health informatics pipelines. Statistical significance of classifier accuracy differences between CHIT and MICE was assessed using McNemar’s test (two-tailed, continuity-corrected chi-square) applied to the exact binary prediction vectors from each experiment [n_test = 80]. Ninety-five percent confidence intervals for all reported accuracy values were computed using the Wilson score method [z = 1.96].

The 42-configuration mean accuracy and standard deviation for CHIT are reported in Supplementary <a href="plosone.org/article/info:doi/10.1371/journal.pone.0359577#pone.0359577.s001" target="_blank">Table S1</a>. Full McNemar results with confidence intervals are in Supplementary <a href="plosone.org/article/info:doi/10.1371/journal.pone.0359577#pone.0359577.s002" target="_blank">Table S2</a>.</p></div>