Background: Several discriminating techniques have been proposed to discriminate between β‐thalassemia trait (βTT) and iron deficiency anemia (IDA) so far. These discrimination techniques are important clinically, but they are challenging and normally difficult; so if a patient with IDA is diagnosed as βTT, then it is deprived of iron therapy. This study is the first application of the Bayesian tree-based method for differential diagnosis of βTT from IDA.
Method: In this study, 907 patients were enrolled with the ages over 18-year-old with microcytic anemia. Bayesian Logit Treed (BLTREED) has been used to discriminate βTT from IDA.
Results: Mean corpuscular volume (MCV) was found as the main predictor in diagnostic discrimination. BLTREED model showed high sensitivity (96%), specificity (93%), accuracy (95%), Youden's index (89), as well as positive and negative predictive values in the differential diagnosis of βTT from IDA. Also, AUC revealed a more precise classification with an area under the curve value of 0.98.
Conclusions: BLTREED model showed excellent diagnostic accuracy for differentiating βTT from IDA. In addition, understanding tree-based methods are easy and need not a statistical experience, so this advantage can help physicians in making the right clinical decision. Thus, we suggest the using of the BLTREED model as a powerful method in data mining techniques in order to develop sensitive and accurate diagnostic methods for for discriminating between these two anemia disorders.