Khaki Jamei, M., Mirzaei Talarposhti, K. (2016). Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS-ANN). Journal of Advances in Computer Research, 7(4), 55-66.
Mehrzad Khaki Jamei; Khadijeh Mirzaei Talarposhti. "Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS-ANN)". Journal of Advances in Computer Research, 7, 4, 2016, 55-66.
Khaki Jamei, M., Mirzaei Talarposhti, K. (2016). 'Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS-ANN)', Journal of Advances in Computer Research, 7(4), pp. 55-66.
Khaki Jamei, M., Mirzaei Talarposhti, K. Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS-ANN). Journal of Advances in Computer Research, 2016; 7(4): 55-66.
Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS-ANN)
Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
Abstract
Discrimination between iron deficiency (IDA) anemia and β-thalassemia trait (β-TT) is a time consuming and costly problem. Because, they have approximate similar effects on routine blood test indices, in some cases, the complementary tests, which are expensive and time consuming, would be needed for differentiate the anemia. Complete blood count (CBC) is a fast, inexpensive, and accessible medical test that is used as a primary test for diagnosis anemia. However, when the CBC indices cannot exactly state the subject, more advanced tests such as electrophoresis of hemoglobin must be performed. In this study, the CBC indices have been considered as the inputs of classifier and the chosen architecture is pattern-based input selection artificial neural network (PBIS-ANN). For evaluation the proposed method, traditional methods, which are still using for the problem such as Mentzer Index (MI), and several automated anemia diagnostic systems such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) have been compared with the proposed method. The results indicate that the proposed method significantly outperforms the mentioned methods.
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