Diagnostic performance of discriminant formulas and machine learning models for detecting β-thalassemia trait in Bangladesh

Abstract

Background β-thalassemia poses a considerable public health burden in Bangladesh, where a high carrier frequency underlies widespread disease risk. It is necessary to distinguish β-thalassemia trait (βTT) and iron deficiency anemia (IDA) to ensure genetic counseling and enable effective prevention strategies. Despite the availability of various discriminant formulas and machine learning algorithms (MLAs), their comparative diagnostic performance within the Bangladeshi population has not been comprehensively investigated. This study aimed to assess different discriminant formulas and ML models as well as to propose novel combinations of formulas for population-specific screening of βTT. Methods In this cross-sectional study, we compared 47 discriminant formulas and 12 machine learning models to distinguish β-thalassemia trait from iron-deficiency anemia in 467 individuals (143 βTT, 324 anemia) drawn from a 2,514-participant cohort. DF-6 and DF-27 were two new formulas constructed by integrating high-performing formulas. Multi-criteria decision-making (MCDM) techniques, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SECA (Simultaneous Evaluation of Criteria and Alternatives), provided the final ranking for performance. Cluster analysis was performed to identify groups with similar diagnostic performance. Results Population-specific optimal cut-off values were determined for the discriminant formulas. The newly proposed formulas, DF-6 and DF-27, ranked among the top ten performers alongside RBC, Janel (11T), Ravanbakhsh-F1, Srivastav, Alparslan, Hisham, Index 26, and Kerman I. DF-6 (AUC: 0.9707) achieved the best overall performance across the diagnostic metrics. DF-6 achieved the best overall performance (AUC: 0.98, 95% CI: 0.97–0.99, p < 0.0001). Assessment of ML models revealed that XGBoost (XGB) (AUC: 0.98, 95% CI: 0.97–0.99, p < 0.0001) and Support Vector Machine (SVM) (AUC: 0.97, 95% CI: 0.95–0.99, p < 0.0001) provided the highest diagnostic accuracy. The reliability of ensemble ML models was confirmed by MCDM and cluster analyses. Conclusions The combination of novel discriminant formula DF-6 and integration of XGB and SVM ML models can substantially strengthen nationwide screening programs to reduce the burden of thalassemia in Bangladesh.