Eşsiz, U?ur EnginAci, Çi?dem İnanSaraç, EsraAci, Mehmet2025-01-062025-01-0620241453-824510.59277/ROMJIST.2024.3-4.042-s2.0-85206945496https://doi.org/10.59277/ROMJIST.2024.3-4.04https://hdl.handle.net/20.500.14669/1644Vitamin 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.eninfo:eu-repo/semantics/closedAccess25(OH)D levelclassificationdeep learningfeature selectionpredictionvitamin D deficiencyArticle3093-4Q129527