Deep Learning-Based Prediction Models for the Detection of Vitamin D Deficiency and 25-Hydroxyvitamin D Levels Using Complete Blood Count Tests

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dc.contributor.authorEşsiz, U?ur Engin
dc.contributor.authorAci, Çi?dem İnan
dc.contributor.authorSaraç, Esra
dc.contributor.authorAci, Mehmet
dc.date.accessioned2025-01-06T17:30:28Z
dc.date.available2025-01-06T17:30:28Z
dc.date.issued2024
dc.description.abstractVitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single ”sufficient/deficient” classification problem using machine learning or statistics-based methods. The main objective of this paper is to predict a patient’s VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health laboratory was used for this purpose. CNN, RNN, LSTM, GRU and Auto-encoder algorithms were used to develop DL-based models. The top 25 features in the CBC tests were carefully selected by implementing the Extra Trees Classifier and Multi-task LASSO feature selection algorithms. The performance of the models was evaluated using metrics such as accuracy, F1-score, mean absolute error, root mean square error and R-squared. Remarkably, all three models showed satisfactory results when compared to the existing literature; however, the CNN-based prediction models proved to be the most successful. © 2024, Publishing House of the Romanian Academy. All rights reserved.
dc.identifier.doi10.59277/ROMJIST.2024.3-4.04
dc.identifier.endpage309
dc.identifier.issn1453-8245
dc.identifier.issue3-4
dc.identifier.scopus2-s2.0-85206945496
dc.identifier.scopusqualityQ1
dc.identifier.startpage295
dc.identifier.urihttps://doi.org/10.59277/ROMJIST.2024.3-4.04
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1644
dc.identifier.volume27
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPublishing House of the Romanian Academy
dc.relation.ispartofRomanian Journal of Information Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subject25(OH)D level
dc.subjectclassification
dc.subjectdeep learning
dc.subjectfeature selection
dc.subjectprediction
dc.subjectvitamin D deficiency
dc.titleDeep Learning-Based Prediction Models for the Detection of Vitamin D Deficiency and 25-Hydroxyvitamin D Levels Using Complete Blood Count Tests
dc.typeArticle

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