Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system. (May 2020)

• Record Type: Journal Article
• Authors: Çil, Betül; Ayyıldız, Hakan; Tuncer, Taner
• Is Part Of: Medical hypotheses. Volume 138(2020)
• Publication Date: 2020-05
• Subjects: Extreme Learning Machines (ELM) -- β-Thalassemia -- Iron deficiency anemia -- Machine learning; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Medicine; Periodicals; 610
• DOI: 10.1016/j.mehy.2020.109611
• Languages: English
• ISSNs: 0306-9877

Abstract

Abstract: The symptoms of Iron Deficiency Anemia (IDA) and β-thalassemia (β-TT) disease are similar and the distinction between them is time consuming and costly. There are several indices used to differentiate IDA from β-thalassemia disease. Complete Blood Count (CBC) is a rapid, inexpensive and accessible test for the diagnosis of anemia and is used as a primary test. However, since CBC cannot fully distinguish between IDA and β-thalassemia, more advanced testing is required. These tests are not available in small centers and are performed on higher-cost devices. Moreover, it is important to differentiate between anemia and β-thalassemia medically for two reasons (IDA). First, if a patient with β-Thalassemia is diagnosed with IDA, the patient is given unnecessary iron supplementation as a result of the treatment, which is recommended by the doctor. Secondly, when the patient with β-thalassemia is diagnosed with IDA, children will have β-thalassemia patients in marriages. A decision support system to distinguish between β-Thalassemia and IDA has been developed. Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Extreme Learning Machine and Regularized Extreme Learning Machine classification algorithms were used in the proposed system. Classification performance was evaluated with Accuracy, sensitivity, f-measure, Specificty parameters using Hemoglobin, RBC, HCT, MCV, MCH, MCHC and RDW parameters obtained from 342 patients. 96.30% accuracy for female, 94.37% for male, and 95.59% in co-evaluation of male and female patients were obtained.