  In recent decades, attention has been directed at anemia classification for
various medical purposes, such as thalassemia screening and predicting iron
deficiency anemia (IDA). In this study, a new method has been successfully
tested for discrimination between IDA and \b{eta}-thalassemia trait
(\b{eta}-TT). The method is based on a Dynamic Harmony Search (DHS). Complete
blood count (CBC), a fast and inexpensive laboratory test, is used as the input
of the system. Other models, such as a genetic programming method called
structured representation on genetic algorithm in non-linear function fitting
(STROGANOFF), an artificial neural network (ANN), an adaptive neuro-fuzzy
inference system (ANFIS), a support vector machine (SVM), k-nearest neighbor
(KNN), and certain traditional methods, are compared with the proposed method.
