Preprint Article Version 1 This version is not peer-reviewed

Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and Β-Thalassemia with CBC Indices Based on Dynamic Harmony Search (DHS)

Version 1 : Received: 3 March 2020 / Approved: 4 March 2020 / Online: 4 March 2020 (15:34:04 CET)

How to cite: Qasem, S.N.; Mosavi, A. Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and Β-Thalassemia with CBC Indices Based on Dynamic Harmony Search (DHS). Preprints 2020, 2020030071 (doi: 10.20944/preprints202003.0071.v1). Qasem, S.N.; Mosavi, A. Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and Β-Thalassemia with CBC Indices Based on Dynamic Harmony Search (DHS). Preprints 2020, 2020030071 (doi: 10.20944/preprints202003.0071.v1).

Abstract

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 β-thalassemia trait (β-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.

Subject Areas

Anemia classification; dynamic harmony search; iron deficiency anemia; thalassemia trait; machine learning

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