{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:57:13Z","timestamp":1791896233636,"version":"4.3.4"},"reference-count":15,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah","award":["GPIP:1215-611-2024"],"award-info":[{"award-number":["GPIP:1215-611-2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Vitamin D deficiency (VDD) has emerged as a serious global health concern that can lead to far-reaching consequences, including skeletal issues and long-term illness. Classical diagnostic approaches, although effective, often include invasive techniques and lacks to leverage the massive amount of healthcare data. There is an increasing demand for noninvasive prediction approaches for determining the severity of VDD. This work proposes a novel approach to detect VDD levels by combining deep learning techniques with evolutionary computing (EC). Specifically, we employ a hybrid deep learning model that includes convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to predict VDD data effectively. To improve the models effectiveness and guarantee the optimal choice of the features and hyper-parameters, we incorporate evolutionary computing methods, particularly genetic algorithms (GA). The proposed method has been proven effective through a comprehensive assessment on a benchmark dataset, with 97% accuracy, 96% precision, 97% recall, and 96% F1-score. Our approach yielded improved performance, when compared to earlier methods. This research not only push forward predictive healthcare models but also shows the potential of merging deep learning with evolutionary computing to address intricate health-care issues.<\/jats:p>","DOI":"10.7717\/peerj-cs.2698","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T08:40:46Z","timestamp":1740127246000},"page":"e2698","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing"],"prefix":"10.7717","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5010-3249","authenticated-orcid":true,"given":"Ahmed","family":"Alzahrani","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3320-2074","authenticated-orcid":true,"given":"Muhammad Zubair","family":"Asghar","sequence":"additional","affiliation":[{"name":"Gomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal University, Dera Ismail Khan, KP, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"issue":"3","key":"10.7717\/peerj-cs.2698\/ref-1","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.bone.2009.05.012","article-title":"25-hydroxy vitamin D levels in healthy premenopausal women: association with bone turnover markers and bone mineral density","volume":"45","author":"Adami","year":"2009","journal-title":"Bone"},{"issue":"1","key":"10.7717\/peerj-cs.2698\/ref-2","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.1109\/TCE.2023.3325224","article-title":"Maintaining user security in consumer electronics-based online recommender systems using federated learning","volume":"70","author":"Alzahrani","year":"2023","journal-title":"IEEE Transactions on Consumer Electronics"},{"issue":"1","key":"10.7717\/peerj-cs.2698\/ref-3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s00223-019-00559-4","article-title":"Vitamin D deficiency: defining, prevalence, causes, and strategies of addressing","volume":"106","author":"Cashman","year":"2020","journal-title":"Calcified Tissue International"},{"issue":"4","key":"10.7717\/peerj-cs.2698\/ref-4","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/s11936-012-0183-8","article-title":"Vitamin D and cardiovascular disease","volume":"14","author":"Danik","year":"2012","journal-title":"Current Treatment Options in Cardiovascular Medicine"},{"issue":"6","key":"10.7717\/peerj-cs.2698\/ref-5","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1002\/ijc.25439","article-title":"Meta-analysis of observational studies of serum 25-hydroxyvitamin D levels and colorectal, breast and prostate cancer and colorectal adenoma","volume":"128","author":"Gandini","year":"2011","journal-title":"International Journal of Cancer"},{"issue":"1","key":"10.7717\/peerj-cs.2698\/ref-6","doi-asserted-by":"publisher","first-page":"2350055","DOI":"10.1142\/S0219649223500557","article-title":"VitaDNet: a deep learning-based approach for vitamin-D deficiency prediction","volume":"23","author":"Kasyap","year":"2024","journal-title":"Journal of Information & Knowledge Management"},{"issue":"5","key":"10.7717\/peerj-cs.2698\/ref-7","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/s00774-018-0984-2","article-title":"A simple questionnaire for the prediction of vitamin D deficiency in Japanese adults (Vitamin D Deficiency questionnaire for Japanese: VDDQ-J)","volume":"37","author":"Kuwabara","year":"2019","journal-title":"Journal of Bone and Mineral Metabolism"},{"issue":"4","key":"10.7717\/peerj-cs.2698\/ref-8","doi-asserted-by":"publisher","first-page":"616","DOI":"10.3390\/math10040616","article-title":"Predictive ability of machine-learning methods for vitamin D deficiency prediction by anthropometric parameters","volume":"10","author":"Patino-Alonso","year":"2022","journal-title":"Mathematics"},{"issue":"20","key":"10.7717\/peerj-cs.2698\/ref-9","doi-asserted-by":"publisher","first-page":"4541","DOI":"10.1049\/gtd2.12943","article-title":"A security model for smart grid SCADA systems using stochastic neural network","volume":"17","author":"Rabie","year":"2023","journal-title":"IET Generation, Transmission & Distribution"},{"key":"10.7717\/peerj-cs.2698\/ref-10","first-page":"136","article-title":"Efficient diagnoses of breast cancer disease using deep learning technique","author":"Raza","year":"2024"},{"issue":"3","key":"10.7717\/peerj-cs.2698\/ref-11","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.endinu.2016.11.009","article-title":"Efectosinmunometab\u00f3licosdisfuncionales de la deficiencia de vitamina D y aumento de riesgocardiometab\u00f3lico. 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