Abstract
Abstract
Background
Machine learning (ML), with advancements in algorithms and computations, is seeing an increased presence in life science research. This study investigated several ML models' efficacy in predicting preterm birth using untargeted metabolomics from serum collected during the third trimester of gestation.
Methods
Samples from 48 preterm and 102 term delivery mothers from the All Our Families Cohort (Calgary, AB) were examined. Four ML algorithms: Partial Least Squares Discriminant Analysis (PLS-DA), linear logistic regression, artificial neural networks (ANN), Extreme Gradient Boosting (XGBoost) - with and without bootstrap resampling were used to examine the small-scale clinical dataset for both model performance and metabolite interpretation.
Results
Model performance was evaluated based on confusion matrices, area under the receiver operating characteristic (AUROC) curve analysis, and feature importance rankings. Linear models such as PLS-DA and logistic regression demonstrated moderate classification performance (AUROC ≈ 0.60), whereas non-linear approaches, including ANN and XGBoost, exhibited marginal improvements. Among all models, XGBoost combined with bootstrap resampling achieved the highest performance, yielding an AUROC of 0.85 (95 % CI: 0.57–0.99, p
Conclusions
Our results highlight the complexity of metabolomics-based modelling for preterm birth and support an iterative, model-driven approach for optimizing predictive accuracy in small-scale clinical datasets.