The TVGH-NYCU thal-classifier: development of a machine-learning classifier for differentiating thalassemia and non-thalassemia patients
Open accessAbstract
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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The majority of existing ML models or rule-based formulas specifically differentiate one distinct thalassemia (α or β) from IDA.
“The majority of these formulas specifically differentiate one distinct thalassemia (α or β) from IDA (e.g., [14][15][16][17][18][19].”
From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [16]
Existing models are primarily designed using small single-center data sets lacking independent validation.
“Moreover, these models are primarily designed using small single-center data sets lacking independent validation (15,16,18,(33)(34)(35)(36).”
From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [16]
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- Authors
- Fu, Y.-K., Liu, H.-M., Lee, L.-H., Chen, Y.-J., Chien, S.-H., Lin, J.-S., Chen, W.-C., Cheng, M.-H., Lin, P.-H., Lai, J.-Y., Chen, C.-M., & Liu, C.-Y.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- Taipei Veterans General Hospital (TW)
- School of Medicine, Yangming Campus, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Corresponding author
- Chen, Chyong-Mei
- Liu, Chun-Yu
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2021-09-20
- Volume
- 11
- Issue
- 9
- Pages
- 1725
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC8467438
- OpenAlex
- W3201291511
- Cited by
- 38
- References
- 35
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 6 · 2023: 10 · 2022: 4
- FWCI
- 2.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Taipei Veterans General Hospital (V110C-180)
- Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST110-G5-1-3)
- Topics
- Health Sciences › Medicine › Genetics › Hemoglobinopathies and Related Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- thalassemia, microcytic anemia, iron deficiency anemia, differential diagnosis, support vector classifier, primary care diagnosis, Taiwanese adults
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