Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction
Bibliographic record
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 < 0.001), indicating a significant improvement in classification accuracy. Metabolite importance, derived from Shapley Additive Explanations (SHAP), consistently identified acylcarnitines and amino acid derivatives as principal discriminative features. Pathway analysis revealed disruptions to tyrosine metabolism as well as phenylalanine, tyrosine and tryptophan biosynthesis to be associated with preterm delivery. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".