A New Computer-Aided Diagnostic Method for Classifying Anemia Disease:
Hybrid Use of Tree Bagger and Metaheuristics
Abstract
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.