Machine Learning-Based Prediction of β-Thalassemia Trait Using Red Blood Cell Indices

Objectives: Anemia is a significant global health concern, with hypochromic microcytic anemia being the most common type. Among its causes, β-thalassemia trait (β-TT) and iron deficiency anemia (IDA) share similar hematological features, making differentiation challenging. We aimed to develop machine learning (ML) models using routine red blood cell (RBC) indices to distinguish β-TT, IDA, and healthy cases. Methods: A total of 8106 individuals (3378 healthy, 2696 IDA, 2032 β-TT) were included in this study. Six RBC parameters—RBC count, hemoglobin (HGB), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), and RBC distribution width (RDW-CV)—were used to train eXtreme gradient boosting (XGB), random forest (RF), and neural network (NN) models. The dataset was split into training (70%) and testing (30%) sets, with feature importance assessed via the Boruta algorithm. All statistical analyses were performed using R version 4.3.1 Statistical Language. Results: All models achieved high accuracy (> 97%), with RF demonstrating superior performance (97.86% accuracy, 99.71% AUC). The most significant features contributing to the models are MCH for the XGB algorithm, MCV for the RF algorithm, and HGB for the NN algorithm. Conclusions: Our findings demonstrate that ML-based models could offer a promising tool for improving β-TT detection, optimizing clinical workflows, and enhancing resource utilization in hematology.