2020/03/03 by Sultan Noman Qasem, Qasem, Sultan Noman, Amir Mosavi +1
Computer Science · Health Professions · Mathematics · #68T05 #Artificial Intelligence in Healthcare #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.LG #msc:68T05 #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.00480
10pages, 5 figures, 7 tables
arxiv created 2020/03/03 · openalex publication_date 2020/03/03 · arxiv updated 2020/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 \beta-thalassemia trait (\beta-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.