Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing
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 he...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-02-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2698.pdf |
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| author | Ahmed Alzahrani Muhammad Zubair Asghar |
| author_facet | Ahmed Alzahrani Muhammad Zubair Asghar |
| author_sort | Ahmed Alzahrani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c60b7863077f4063adb2e1bfd6b1e2b2 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-c60b7863077f4063adb2e1bfd6b1e2b22025-08-20T02:15:19ZengPeerJ Inc.PeerJ Computer Science2376-59922025-02-0111e269810.7717/peerj-cs.2698Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computingAhmed Alzahrani0Muhammad Zubair Asghar1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaGomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal University, Dera Ismail Khan, KP, PakistanVitamin 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.https://peerj.com/articles/cs-2698.pdfCNN+BILSTMDeep learningVitamin D deficiencyEvolutionary computingGenetic algorithm |
| spellingShingle | Ahmed Alzahrani Muhammad Zubair Asghar Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing PeerJ Computer Science CNN+BILSTM Deep learning Vitamin D deficiency Evolutionary computing Genetic algorithm |
| title | Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing |
| title_full | Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing |
| title_fullStr | Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing |
| title_full_unstemmed | Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing |
| title_short | Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing |
| title_sort | enhancing the prediction of vitamin d deficiency levels using an integrated approach of deep learning and evolutionary computing |
| topic | CNN+BILSTM Deep learning Vitamin D deficiency Evolutionary computing Genetic algorithm |
| url | https://peerj.com/articles/cs-2698.pdf |
| work_keys_str_mv | AT ahmedalzahrani enhancingthepredictionofvitaminddeficiencylevelsusinganintegratedapproachofdeeplearningandevolutionarycomputing AT muhammadzubairasghar enhancingthepredictionofvitaminddeficiencylevelsusinganintegratedapproachofdeeplearningandevolutionarycomputing |