Designing an expert system for differential diagnosis of β-Thalassemia minor and Iron-Deficiency anemia using neural network


Rahil Hosseini Eshpala 1 , Mostafa Langarizadeh 2 , * , Mehran Kamkar Haghighi 3 , Banafsheh Tabatabaei 4

1 MSc Student Department of Medical Information ,Iran University of Medical Sciences, Tehran, Iran

2 Assistant Professor Department of Medical Information,Iran University of Medical Sciences, Tehran, Iran

3 Instructor Department of Health Information Management,Iran University of Medical Sciences, Tehran, Iran

4 General Physician , Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

How to Cite: Hosseini Eshpala R , Langarizadeh M , Kamkar Haghighi M , Tabatabaei B . Designing an expert system for differential diagnosis of β-Thalassemia minor and Iron-Deficiency anemia using neural network, Hormozgan Med J. 2013 ; 20(1):e87905.


Hormozgan Medical Journal: 20 (1); e87905
Published Online: January 28, 2015
Article Type: Research Article
Received: July 15, 2014
Accepted: January 28, 2015


Introduction: Artificial neural networks are a type of systems that use very complex technologies and non-algorithmic solutions for problem solving. These characteristics make them suitable for various medical applications. This study set out to investigate the application of artificial neural networks for differential diagnosis of thalassemia minor and iron-deficiency anemia.

Methods: It is a developmental study with a cross-sectional-descriptive design. The statistical population included CBC results of 395 individuals visiting for premarital tests from 21 March to 21 June, 2013. For development of the neural network, MATLAB 2011 was used. Different training algorithms were compared after error propagation in the neural network. Finally, the best network structure (concerning diagnostic sensitivity, specificity, and accuracy) was selected, using the confusion matrix and the receiver operating characteristic (ROC).

Results: The proposed system was based on a multi-layer perceptron algorithm with 4 inputs, 100 neurons, and 1 hidden layer. It was used as the most powerful differential diagnosis instrument with specificity, sensitivity and accuracy of 92%, 94%, and 93.9%, respectively.

Conclusion: The artificial neural networks have powerful structures for categorizing data and learning the patterns. Among different training methods, the Levenberg-Marquardt backpropagation algorithm produced the best results due to faster convergence in network training. It also showed considerable accuracy in differentiating patients from healthy individuals. The proposed method allows accurate, correct, timely, and cost-effective diagnoses. In line with the application of intelligent expert systems, development of this system is presented as a new outlook for medical systems.



  • 1.

    Refrences are included in the PDF.

  • © 2013, Hormozgan Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.