ArticleJournal of medical biochemistry2024
Artificial intelligence-driven diagnosis of β-thalassemia minor & iron deficiency anemia using machine learning models.
Article in Journal of medical biochemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Comparison of the diagnostic performance of machine learning algorithms for differentiating iron deficiency anemia and thalassemia.Annals of hematology · 2026Article
- Prediction ofFrontiers in medicine · 2026Article
- Anaemia: A Scientific Overview on Types and Recent Advancements in the Detection of Anaemia.Cardiovascular & hematological agents in medicinal chemistry · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Machine learning improves detection of alpha thalassemia carriers compared to clinical features.Scientific reports · 2025Article
- Automated Morphologic Differentiation Between Iron Deficiency Anemia and Thalassemia.Journal of clinical laboratory analysis · 2025Article
- From CBC to clarity: Interpretable detection of beta-thalassemia carriers in imbalanced datasets.PloS one · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data. Methods: This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections.
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Registered trials
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