ArticleScientific reports2025
Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Development and Internal Validation of a Machine Learning-Based Model for Thalassemia Identification Using Complete Blood Count Parameters.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- Early prediction of severe Omicron pneumonia using a multimodal a.i. model integrating delta CT radiomics and laboratory indicators.Scientific reports · 2026Article
- Predicting Iron Deficiencies Using Routine Complete Blood Cell Count Parameters: A Machine Learning Approach and Evaluation.Journal of clinical medicine · 2026Article
- Beyond prediction: explainable machine learning for ferritin estimation from complete blood count.BMC medical informatics and decision making · 2026Article
- A deep dive into ferritin nanoparticle advancements: experimental and computational perspectives.Discover nano · 2026Review
- 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
- AI-assisted haematology: machine learning-based prediction of iron-deficiency anaemia from reticulocyte maturation indices.BMC medical informatics and decision making · 2025Article
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4 authors.
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Abstract
Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they exhibit varying efficiencies. In this study, we aimed to develop a tool for discriminating between IDA and Thal by using the random forest (RF) and gradient boosting (GB) algorithms. Complete blood count data from 1143 patients with anemia and low mean corpuscular volume were collected (382 patients with IDA, 635 with Thal, and 126 with IDA and Thal). The data were randomly divided into the training and testing datasets in a ratio of 80:20. The RF and GB models had good diagnostic performances for predicting IDA and Thal in the training and testing datasets. In the testing dataset for predicting binary outcomes, GB and RF both had an accuracy of 90.7%, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.953. A lower diagnostic performance was observed when patients with IDA and Thal were included. GB and RF showed accuracies of 80.4% and 82.2%, respectively, and AUC-ROC values of 0.910 and 0.899, respectively. In conclusion, we developed a machine learning approach using GB algorithm. This tool is potentially useful in Thal- and IDA-endemic regions.
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