Classification of α-thalassemia data using machine learning models

ML models outperformed classical discriminant formulae in classifying α-thalassemia using sex and CBC features. A larger dataset could enhance ML model generalization and the impact of feature extraction. Grouping silent- and non-carriers improved ML results, especially with resampling. The silent carriers were not separable from non-carriers regarding the available features.