Development of an interpretable ensemble learning model for thalassemia detection in pregnant women using routine...

Thalassemia is a common hereditary anemia that poses diagnostic challenges during pregnancy due to physiological changes in hematological parameters. Misdiagnosis or delayed diagnosis can result in serious maternal and fetal complications. Although genetic testing is the gold standard for thalassemia diagnosis, its high cost and limited availability restrict its use in primary care settings. This study aimed to develop and validate a clinically interpretable machine learning model to identify thalassemia in pregnant women using routine complete blood count indicators.

A total of 523 pregnant patients were retrospectively enrolled in this study. Eight predictive features were selected using the Boruta algorithm. Multiple machine learning algorithms were trained and evaluated using ten-fold cross-validation. An ensemble model was constructed through bagging and weighted integration of top-performing models. Model performance was assessed using a series of metrics such as AUROC, AUPRC, accuracy, F1-score, and MCC. Comparative evaluation was conducted against traditional diagnostic indices and a recently proposed model. Model interpretability was analyzed using permutation importance and SHAP methods.

The Thal-classifier, constructed by integrating four bagged machine learning models, was developed for accurate identification of thalassemia. It exhibited excellent diagnostic performance, with an AUROC of 0.945 on the cross-validation and 0.899 on the independent testing dataset. Compared with the existing indices and models, it achieved superior performance across multiple evaluation metrics. Feature importance analyses consistently identified RDW-SD and MCHC as the primary predictive features.

Thal-classifier is a practical and reliable tool for early detection of thalassemia in pregnant populations, offering valuable support for prenatal screening and individualized clinical decision-making.