Comparative Machine Learning Models for Early Prediction of Preterm Birth from Maternal Serum Biomarkers

Background Preterm birth (PTB) is a major cause of neonatal morbidity and mortality. Inflammation and metabolic disruption are involved in its pathology. This study aimed to assess maternal serum inflammatory and lipid markers as predictors of preterm birth using various machine learning models. Methods Women who were pregnant and attending antenatal clinics were recruited for this study. A group of 186 females who had their births before 37 weeks was marked PTB. The 140 control term deliveries were selected at random. T-tests were used to evaluate variations in baseline and clinical parameters Pearson correlations were visualized via a heatmap. We built models for random forests (RF), logistic regression (LR), XGBoost, and support vector machine (SVM) using a 70/30 train/test split and 5-fold cross-validation. Model performance was measured using accuracy and AUC. Results CRP (r ≈ 0.45), IL-6 (r ≈ 0.40), C3 (r ≈ 0.31), BMI, and lipids correlated positively with PTB, whereas HDL correlated inversely (r ≈ − 0.13). Multivariable logistic regression identified age, BMI, IL-6, C3, and CRP as independent predictors. All ML models showed good discrimination (test AUC ≥ 0.819); logistic regression performed best (accuracy 78.57%, AUC 0.849) with cross-validated AUCs around 0.86–0.87 across models. SHAP analysis confirmed that IL-6, BMI, CRP, age, and C3 were dominant contributors to PTB risk. Conclusions Maternal inflammation and high BMI are important risk factors for preterm birth in this cohort. The logistic regression model combining clinical and serum measures is as good a predictor as complex ML algorithms. It is an interpretable model that can help with risk assessment at an early stage in similar settings.