Establishment and verification of the model in diagnosis of thalassemia trait based on red blood cell parameters: A two-center retrospective study

Objectives Thalassemia trait (TT) screening in resource-limited settings is hampered by reliance on expensive and complex tests. This study aimed to develop and validate a highly accessible machine learning-based tool using only routine blood parameters to accurately differentiate TT from non-TT and its major subtypes. Methods The retrospective study included 987 individuals (221 α-TT, 211 β-TT and 555 non-TT) from two medical centers. Seven machine learning methods—Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron, XGBoost, and CatBoost—were employed to develop diagnostic models, which were evaluated using accuracy, sensitivity, specificity, AUC, PPV, NPV, and F1 score. Results The CatBoost model emerged as superior for differentiating TT from non-TT, achieving an AUC of 0.976, accuracy of 0.940, and specificity of 0.981. It also outperformed other models in distinguishing α-TT from β-TT (AUC = 0.842). Critically, this high-performance model was successfully deployed as a user-friendly WeChat mini-program AI Lab, for real-world clinical application. Conclusion The deployed ML-based AI Lab represents a robust, interpretable, and scalable tool poised to enhance TT screening efficiency and accessibility, particularly in underserved healthcare environments.