loading page

A New Computer-Aided Diagnostic Method for Classifying Anemia Disease: Hybrid Use of Tree Bagger and Metaheuristics
  • Nagihan Yagmur,
  • İdiris Dag,
  • Hasan TEMURTAŞ
Nagihan Yagmur
Eskisehir Osmangazi University

Corresponding Author:[email protected]

Author Profile
İdiris Dag
Eskisehir Osmangazi University
Author Profile
Kutahya Dumlupinar University
Author Profile


Anemia occurs when the hemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision-support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, three hybrid models are proposed to classify non-anemia records, Hgb-anemia, folate deficiency anemia (FDA), iron deficiency anemia (IDA), and B12 deficiency anemia by combining artificial intelligence and machine learning methods TreeBagger with Crow Search Algorithm (CSO), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models aim to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anemia classification problem, fuzzy logic-based parameter optimization is applied to improve the class-based accuracy as well as the overall accuracy in the dataset. The classification performances of the proposed methods are evaluated using accuracy, F-score, precision, and recall criteria to build a prediction model to identify the anemia type of anemic patients. A result of the study on the dataset taken from the Kaggle database found that the three proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.
11 Aug 2023Submitted to Expert Systems
14 Aug 2023Assigned to Editor
14 Aug 2023Submission Checks Completed
15 Aug 2023Reviewer(s) Assigned
18 Sep 2023Review(s) Completed, Editorial Evaluation Pending
18 Sep 2023Editorial Decision: Revise Major