biostudies-literatureUnknown16(1)Tian THypertrophic scarring (HS) following severe burns remains a persistent rehabilitative challenge, yet traditional linear prediction models fail to capture the non-linear pathophysiological complexity of fibrosis. This study aimed to engineer an interpretable machine learning framework to stratify HS risk and elucidate its driving mechanisms. Utilizing a retrospective cohort of 520 severe burn patients, we benchmarked four machine learning algorithms, selecting Extreme Gradient Boosting (XGBoost) for model construction. The SHapley Additive exPlanations (SHAP) framework was integrated to decode algorithmic decision-making, specifically analyzing feature contributions and interaction effects. We benchmarked four machine learning algorithms, selecting Extreme Gradient Boosting (XGBoost) for moScientific reports6908https://www.ebi.ac.uk/biostudies/studies/S-EPMC12916780biostudies-literatureInterpretable machine learning unveils non-linear inflammatory thresholds and synergistic interactions in post-burn hypertrophic scarring: development of an intelligent clinical decision support system.PMC12916780Ji GTian TLiu SfalseInterpretable machine learning unveils non-linear inflammatory thresholds and synergistic interactions in post-burn hypertrophic scarring: development of an intelligent clinical decision support system.Hypertrophic scarring (HS) following severe burns remains a persistent rehabilitative challenge, yet traditional linear prediction models fail to capture the non-linear pathophysiological complexity of fibrosis. This study aimed to engineer an interpretable machine learning framework to stratify HS risk and elucidate its driving mechanisms. Utilizing a retrospective cohort of 520 severe burn patients, we benchmarked four machine learning algorithms, selecting Extreme Gradient Boosting (XGBoost) for model construction. The SHapley Additive exPlanations (SHAP) framework was integrated to decode algorithmic decision-making, specifically analyzing feature contributions and interaction effects. We benchmarked four machine learning algorithms, selecting Extreme Gradient Boosting (XGBoost) for mo2026-01-01T00:00:00Z2026 Feb2026-07-09T11:25:14.862Z2026-07-09T10:46:32.621ZS-EPMC129167804162957110.1038/s41598-026-36868-6