Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data |
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Authors: | Iman Azarkhish Mohammad Reza Raoufy Shahriar Gharibzadeh |
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Institution: | (1) Amirkabir University of Technology, Tehran, Iran; |
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Abstract: | Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming,
expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC),
we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and
to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression
models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum
iron levels with high accuracy and acceptable precision. |
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Keywords: | |
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