Comparison of Interpretable Machine Learning Models Using Systemic Inflammation Index to Predict Preterm Birth in Gestational Diabetes Mellitus

Qinxia Pang,* Lei Peng,* Jianfa Wu, Ying Wang, Rong Zhang, Zhou Liu, Lingli Jiang Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lingli Jiang, Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences affiliated Zhoupu Hospital, No. 1500, Zhouyuan Road, Pudong New Area, Shanghai, 201318, People’s Republic of China, Tel +86-13817459299, Email [email protected] Zhou Liu, Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences affiliated Zhoupu Hospital, No. 1500, Zhouyuan Road, Pudong New Area, Shanghai, 201318, People’s Republic of China, Tel +86-18930837705, Email [email protected]: Gestational diabetes mellitus (GDM) elevates preterm birth risk, highlighting the need for improved prediction methods to enhance outcomes. Current models show limited accuracy by ignoring some inflammatory biomarkers (eg, PLR, LMR, SII). Machine learning (ML) can better analyze complex patterns but remains underused for GDM preterm birth prediction.Objective: This study develops an interpretable ML model combining systemic inflammatory indices and traditional clinical markers to predict preterm birth in GDM. Enabling early risk stratification at diagnosis, it facilitates timely interventions for this high-risk population.Methods: This retrospective study analyzed 389 GDM patients, stratified into training (n=272) and temporal external validation (n=117) cohorts, and further classified by birth outcome (term/preterm). Using the training cohort, we developed and internally validated multiple ML models incorporating: (1) systemic inflammation indices, (2) traditional clinical indicators, and (3) their combination. The optimal model underwent temporal external validation and subsequent Shapley Additive Explanations (SHAP) analysis for feature interpretation. To assess the robustness of our findings, sensitivity analyses were conducted.Results: Our cohort of 389 GDM patients included 53 preterm births (13.6%). Analysis revealed seven significant predictors combining systemic inflammatory markers and traditional clinical parameters. The extreme gradient boosting (XGBoost) model outperformed comparative algorithms (AUC-ROC: 0.932 vs Logit: 0.871, SVM: 0.847, RF: 0.917; AUC-PRC: 0.754 vs Logit: 0.686, SVM: 0.582, RF: 0.670). SHAP analysis identified five key determinants (two clinical and three inflammatory markers) as most influential for preterm birth prediction. Sensitivity analyses were conducted to assess the robustness of the results.Conclusion: The XGBoost model outperforms in predicting GDM-related preterm birth by integrating traditional clinical and systemic inflammatory markers, enabling precise risk assessment to guide clinical management.Keywords: machine learning, preterm birth, gestational diabetes mellitus, systemic inflammation index