ArticleDiagnostics (Basel, Switzerland)2021
The TVGH-NYCU Thal-Classifier: Development of a Machine-Learning Classifier for Differentiating Thalassemia and Non-Thalassemia Patients.
Article in Diagnostics (Basel, Switzerland), 2021. 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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11 citing papers in PubMed, 34 citations in OpenAlex.
- Comparison of the diagnostic performance of machine learning algorithms for differentiating iron deficiency anemia and thalassemia.Annals of hematology · 2026Article
- Diagnostic performance of discriminant formulas and machine learning models for detecting β-thalassemia trait in Bangladesh.PloS one · 2026Article
- Explainable AI framework for improved Thalassemia mental health classification and feature selection.PloS one · 2026Article
- Machine learning improves detection of alpha thalassemia carriers compared to clinical features.Scientific reports · 2025Article
- Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning.Scientific reports · 2025Article
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
- 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
- A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia.Healthcare (Basel, Switzerland) · 2023Article
- Novel Decision Tool for More Severe α-Thalassemia Genotypes Screening with Functional Loss of Two or More α-Globin Genes: A Diagnostic Test Study.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
Thalassemia and iron deficiency are the most common etiologies for microcytic anemia and there are indices discriminating both from common laboratory simple automatic counters. In this study a new classifier for discriminating thalassemia and non-thalassemia microcytic anemia was generated via combination of exciting indices with machine-learning techniques. A total of 350 Taiwanese adult patients whose anemia diagnosis, complete blood cell counts, and hemoglobin gene profiles were retrospectively reviewed. Thirteen prior established indices were applied to current cohort and the sensitivity, specificity, positive and negative predictive values were calculated. A support vector machine (SVM) with Monte-Carlo cross-validation procedure was adopted to generate the classifier. The performance of our classifier was compared with original indices by calculating the average classification error rate and area under the curve (AUC) for the sampled datasets. The performance of this SVM model showed average AUC of 0.76 and average error rate of 0.26, which surpassed all other indices. In conclusion, we developed a convenient tool for primary-care physicians when deferential diagnosis contains thalassemia for the Taiwanese adult population. This approach needs to be validated in other studies or bigger database.
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