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Journal of Medical Systems
Published in: Journal of Medical Systems 3/2012

01-06-2012 | ORIGINAL PAPER

Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data

Authors: Iman Azarkhish, Mohammad Reza Raoufy, Shahriar Gharibzadeh

Published in: Journal of Medical Systems | Issue 3/2012

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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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Metadata
Title
Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data
Authors
Iman Azarkhish
Mohammad Reza Raoufy
Shahriar Gharibzadeh
Publication date
01-06-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 3/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-011-9668-3

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