{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:17:15Z","timestamp":1793276235167,"version":"4.5.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004625","name":"Universiti Teknologi MARA","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004625","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Br Dent J"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:bold>Background<\/jats:bold>\n                    Dental caries is a chronic disease that requires intervention to prevent complications and minimise costs. Accurate caries risk assessment is crucial but traditional methods depend on skilled clinicians, limiting scalability. Machine learning offers an efficient and standardised alternative.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Objective<\/jats:bold>\n                    This study aimed to develop and evaluate computational models using machine learning techniques for predicting caries risk in adults.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods<\/jats:bold>\n                    A systematic review identified seven predictors that were applied to 3,000 balanced Universiti Teknologi MARA patient records spanning low, moderate and high caries risk. Seven algorithms (decision tree, XGBoost, k-nearest neighbors, logistic regression, multi-layer perceptron, random forest, and support vector machine) were tuned with K-fold cross-validation and stacking ensembles to enhance performance.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results<\/jats:bold>\n                    The two-model stacking approach achieved the highest accuracy (95.17%) and ROC-AUC (receiver operating characteristic-area under the curve) (99.78%), followed by the three-model stacking with a strong accuracy of 93.63% and specificity (96.82%). Among single models, random forest and extreme gradient boosting stood out with an accuracy of 90.47% and 90.20%, respectively, demonstrating robust classification performance.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions<\/jats:bold>\n                    Multi-model machine learning approaches showed high accuracy in caries risk assessment, offering reliable predictions while reducing reliance on extensive clinical judgment. These methods provide standardised risk predictions and support the integration of machine learning into clinical workflows to enhance prevention strategies and patient care.\n                  <\/jats:p>","DOI":"10.1038\/s41415-025-9105-5","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T14:57:13Z","timestamp":1765551433000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Development and evaluation of a multi-model stacking approach for caries risk assessment in adults using supervised machine learning"],"prefix":"10.1038","author":[{"given":"Mohd Hidir Mohd","family":"Atni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nik Mohd Mazuan Nik Mohd","family":"Rosdy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd Azrul Amir Muhamad","family":"Tajudin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Adam","family":"Rusly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor Asilati Abdul","family":"Raob","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Budi Aslinie Md","family":"Sabri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"9105_CR3577","doi-asserted-by":"crossref","unstructured":"GBD 2021 Oral Disorders Collaborators. 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New York: Humana, 2023.","DOI":"10.1007\/978-1-0716-3195-9"}],"container-title":["British Dental Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41415-025-9105-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41415-025-9105-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41415-025-9105-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T14:57:21Z","timestamp":1765551441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41415-025-9105-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":40,"alternative-id":["9105"],"URL":"https:\/\/doi.org\/10.1038\/s41415-025-9105-5","relation":{},"ISSN":["0007-0610","1476-5373"],"issn-type":[{"value":"0007-0610","type":"print"},{"value":"1476-5373","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]},"assertion":[{"value":"1 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that there are no conflicts of interest related to this study. The UiTM Research Ethics Committee approved this retrospective analysis of routinely collected dental records [approval no. REC\/08\/2023 (PG\/MR\/288)]. Since all records were fully de-identified prior to analysis, the Committee waived the requirement for individual informed consent. All data pre-processing and machine learning model training took place entirely on secure, on-premises UiTM servers; no third-party or cloud-based services were used at any stage of the study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declaration"}}]}}