Deep Learning-Based Prediction Models for theDetection of Vitamin D Deficiency and25-Hydroxyvitamin D Levels Using Complete BloodCount Tests
| dc.authorid | ACI, Cigdem/0000-0002-0028-9890 | |
| dc.contributor.author | Essiz, Ugur Engin | |
| dc.contributor.author | Aci, Cigdem Inan | |
| dc.contributor.author | Sarac, Esra | |
| dc.contributor.author | Aci, Mehmet | |
| dc.date.accessioned | 2025-01-06T17:34:53Z | |
| dc.date.available | 2025-01-06T17:34:53Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Vitamin 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. | |
| dc.identifier.endpage | 309 | |
| dc.identifier.issn | 1453-8245 | |
| dc.identifier.issue | 3-4 | |
| dc.identifier.startpage | 295 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14669/1717 | |
| dc.identifier.volume | 27 | |
| dc.identifier.wos | WOS:001339559600004 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.language.iso | en | |
| dc.publisher | Editura Acad Romane | |
| dc.relation.ispartof | Romanian Journal of Information Science and Technology | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241211 | |
| dc.subject | 25(OH)D level | |
| dc.subject | classificationX | |
| dc.subject | classification | |
| dc.subject | deep learning | |
| dc.subject | feature selection | |
| dc.subject | prediction | |
| dc.subject | vitamin D deficiency | |
| dc.title | Deep Learning-Based Prediction Models for theDetection of Vitamin D Deficiency and25-Hydroxyvitamin D Levels Using Complete BloodCount Tests | |
| dc.type | Article |
