A new computer‐aided diagnostic method for classifying anaemia disease: Hybrid use of Tree Bagger and metaheuristics - Yagmur, Nagihan; Dag, Idiris; Temurtas, Hasan (Expert systems, 2024)
Anaemia occurs when the haemoglobin (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, six hybrid models are proposed to classify non‐anaemia records, Hgb‐anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search Algorithm (CSA), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia 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 proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.