Abstract
Iron deficiency anemia (IDA) is a type of microcytic anemia, which is presented by the decrease of mean red cell volume (MCV) as a consequence of reduced hemoglobin (Hb) production. When it comes to diagnosing IDA and the decision making process behind it, physicians have to take into account patients disease history, use of supplements, his current state and lab results, which is why we made an artificial neural network (ANN) that can make the diagnosis easier. The dataset for developing the ANN consisted of 400 samples, of which 150 were healthy individuals and 250 had IDA. The results showed that the ANN with 15 neurons in hidden layer was the best for diagnosis of Iron- deficiency Anemia, with high accuracy, specificity and sensitivity. Even though the dataset was relatively small, the results of ANN development were satisfactory, which indicates that with more samples and therefore much bigger datasets, the accuracy of the developed ANN would increase, which would make the diagnosis of IDA faster and easier.
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Mahir, M., Mahmutović, L., Lovrić, K., Lenjinac, A., Mahić, B., Mačković, N. (2021). Diagnosis of Iron-Deficiency Anemia Using Artificial Neural Network. In: Badnjevic, A., Gurbeta Pokvić, L. (eds) CMBEBIH 2021. CMBEBIH 2021. IFMBE Proceedings, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-73909-6_39
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DOI: https://doi.org/10.1007/978-3-030-73909-6_39
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