Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes

Abstract

Introduction: Iron deficiency anemia (IDA) and β-thalassemia trait (β-TT) are the most common types of microcytic hypochromic anemias. The similarity and the nature of anemia-related symptoms pose a foremost challenge for discriminating between IDA and β-TT. Currently, advances in technology have gave rise to computer-based decision-making systems. Therefore, advances in artificial intelligence have led to the emergence of intelligent systems and the development of tools that can assist physicians in the diagnosis and decision-making. Aim: The aim of the present study was to develop a neural network based model (Artificial Neural Network) for accurate and timely manner of differential diagnosis of IDA and β-TT in comparison with traditional methods. Methods: In this study, an artificial neural network (ANN) model as the first precise intelligent method was developed for differential diagnosis of IDA and β-TT. Data set was retrieved from Complete Blood Count (CBC) test factors of 268 individuals referred to Padad private clinical laboratory at Ahvaz, Iran in 2018. ANN models with different topologies were developed and CBC indices were examined for diagnosis of IDA and β-TT. The proposed model was simulated using MATLAB software package version 2018. The results showed the best network architecture based on the advanced multilayer algorithm (4 input factors, 70 neurons with acceptable sensitivity, specificity, and accuracy). Finally, the results obtained from ANN diagnostic model was compared to existing discriminating indexes. Result: The results of this model showed that the specificity, sensitivity, and accuracy of the proposed diagnostic system were 92.33%, 93.13%, and 92.5%, respectably; i.e. the model could diagnose frequent occurrence of IDA in patients with β-TT. Conclusion: The results and evaluation of the developed model showed that the proposed neural network model has a proper accuracy and generalizability based on the initial factors of CBC testing compared to existing methods. This model can replace the high-cost methods and discriminating indices to distinguish IDA from β-TT and assist in accurate and timely manner diagnosis.

Difference of Het Re Level in Thalassemia Β Minor and Iron Deficiency Anemia

“…Median hemoglobin (Hb) levels of patients with iron deficiency anemia and thalassemia were found to be 9.60% and 9.80% (p 0.0037). The median MCV value and mentzer index of beta thalassemia patients were lower than iron deficiency anemia (61.59; 12.30), while the median RDW value of thalassemia patients was higher (16,60). Table 2.…”

“…The nature of the thalassemic syndrome is associated with reduced or absent synthesis of either of these two chains. The prevalence of α-thalassemia is lower than β-thalassemia and usually does not show symptoms 16 .…”

“…All of these methods result in higher project costs and require specialized instrumentation and trained technicians. In addition, currently various red blood cell (RBC) indexes and formulas have been designed to differentiate between thalassemia and IDA and their sensitivity and specificity are presented in various articles 16 .…”

Difference of Het Re Level In Thalassemia β Minor And Iron Deficiency Anemia

“…The nature of thalasemia syndrome is associated with reduced or absence of synthesis of any of these two chains. The α-thalasemia is lower than β-thalasemia and usually shows no symptoms 16 .…”

“…All of these methods result in higher project costs and require special instrumentation and trained technicians. In addition, currently various indexes and formulas of red blood cells (RBC) have been designed to distinguish between talasemia and IDA and its sensitivity and specificity are presented in various articles 16 .…”