From CBC to clarity: Interpretable detection of beta-thalassemia carriers in imbalanced datasets

Thalassemia is an inherited blood disorder and is among the five most prevalent birth-related complications, especially in Southeast Asia. Thalassemia is classified into two main types—alpha-thalassemia and beta-thalassemia—based on the reduced or absent production of the corresponding globin chains. Over the past couple of decades, researchers have increasingly focused on the application of machine learning algorithms to medical data for identifying hidden patterns to assist in the prediction and classification of diseases and patients. To effectively analyze more complex medical data, more robust machine learning models have been developed to address various health issues. Many researchers have employed different artificial intelligence-based algorithms, i.e., Random Forest, Decision Tree, Support Vector Machine, ensemble-based classifiers, and deep neural networks to accurately detect carriers of beta-thalassemia by training on both diseased and normal test reports. While genetic testing is required by doctors for the most accurate diagnosis, a simple Complete Blood Count (CBC) report can be used to estimate the likelihood of being a beta-thalassemia carrier. Various models have successfully identified beta-thalassemia carriers using CBC data alone, but these models perform classification and prediction based on normalized data. They achieve high accuracy but at the cost of substantial changes to the dataset through class normalization. In this research, we have proposed a Dominance-based Rough Set Approach model to classify patients without balancing the classes (Normal, Abnormal), and the model achieved good performance (91% accuracy). In terms of generalization, the proposed model obtained 89% accuracy on unseen data, comparable to or better than existing approaches.