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

EasyChair Preprint 2849

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.

Keyphrases: Anemia classification, dynamic harmony search, iron deficiency anemia, machine learning, thalassemia trait

@booklet{EasyChair:2849,
  author    = {Sultan Noman Qasem and Amir Mosavi},
  title     = {Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and Β-Thalassemia with CBC Indices Based on Dynamic Harmony Search (DHS)},
  howpublished = {EasyChair Preprint 2849},
  year      = {EasyChair, 2020}}