Prenatal Prediction of Umbilical Cord Blood CD34+ Adequacy and Identifying Influential Factors via An Ensemble Machine Learning and TOPSIS Ranking: A Retrospective Study.

Objective: cells/kg recipient) before collection for single-unit grafts. Materials and Methods: In this retrospective study, we analysed 126,406 records from the Royan Stem Cell Technology Company (RSCT; Tehran, Iran), which included routinely available maternal, neonatal, and family-history variables. A pipeline of imputation (IterativeImputer numeric; SimpleImputer+OrdinalEncoder categorical), feature selection (Extra Trees), and hyperparameter tuning using Bayesian optimisation with model training/evaluation was performed within cross-validation folds. Decision Tree (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) classifiers were tuned via Bayesian optimisation. Models were ranked by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Majority voting (MV) ensembles were constructed from the top-k models. Model interpretability used SHapley Additive exPlanations (SHAP). Results: The MV (top-4: RF, KNN, DT, MLP) ensemble achieved an area under the receiver operating characteristic curve (ROC-AUC)=0.808 and an area under the precision-recall curve (PR-AUC)=0.744 on the held-out test set, with an accuracy=0.757, precision=0.726, recall/sensitivity=0.804, F1=0.762, specificity=0.716, and Brier score=0.181. SHAP highlighted history of hepatitis C, birth place, hyperthyroidism, history of anaemia, oral fungus, and rheumatism among the most influential features. Conclusion: adequacy using an interpretable MV ensemble is feasible and accurate to support pre-collection triage and can potentially improve banking efficiency. The resultant model offers a non-invasive tool to enhance the efficiency of cord blood banking by prioritising units with higher transplantation potential.