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
Affiliations:
Amirkabir University of Technology, Tehran, Iran;Amirkabir University of Technology, Tehran, Iran;Amirkabir University of Technology, Tehran, Iran
Venue:
Journal of Medical Systems
Year:
2012

Citing 3
Cited 0

Artificial nonmonotonic neural networks

Artificial Intelligence
A review of evidence of health benefit from artificial neural networks in medical intervention

Neural Networks
A Novel Method for Diagnosing Cirrhosis in Patients with Chronic Hepatitis B: Artificial Neural Network Approach

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

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.