ArticleActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2019
Differential Diagnosis of Iron-Deficiency Anemia from β-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes.
Article in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
- Article
- A New Formula Based on Simple Blood Indices to Differentiate Beta Thalassemia Trait from Iron Deficiency Anemia.Iranian journal of public health · 2024Article
- Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images.Diagnostics (Basel, Switzerland) · 2022Article
- Iron Deficiency Anemia: Efficacy and Limitations of Nutritional and Comprehensive Mitigation Strategies.Nutrients · 2022Review
- The Evaluation of Results of Twenty Common Equations for Differentiation of Beta Thalassemia Trait from Iron Deficiency Anemia: A Cross-Sectional Study.Iranian journal of public health · 2022Article
- Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2021Article
- Differential diagnosis of thalassemia and iron deficiency anemia in pregnant women using new formulas from multidimensional analysis of red blood cells.Annals of translational medicine · 2021Article
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3 authors.
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Abstract
introductionIron 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.
aimThe 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.
methodsIn 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.
resultThe 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.
conclusionThe 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.
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