Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms

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 grad...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 16917 - 8
Hlavní autoři:	Tepakhan, Wanicha, Srisintorn, Wisarut, Penglong, Tipparat, Saelue, Pirun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 15.05.2025 Nature Publishing Group Nature Portfolio
Témata:	692/308/1426 692/699/1541/13 Adolescent Adult Algorithms Anemia Anemia, Iron-Deficiency - blood Anemia, Iron-Deficiency - diagnosis Blood diseases Boosting Machine Learning Algorithms Cell differentiation Deficiency diseases Diagnosis, Differential Erythrocyte Indices Erythrocytes Female Gradient boosting Humanities and Social Sciences Humans Iron deficiency Iron deficiency anemia Learning algorithms Machine Learning Male Middle Aged multidisciplinary Nutrient deficiency Random Forest ROC Curve Science Science (multidisciplinary) Thalassemia Thalassemia - blood Thalassemia - diagnosis Young Adult Thalassemia Random forest Iron deficiency anemia Machine learning Gradient boosting
ISSN:	2045-2322, 2045-2322
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-01458-5