{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T07:32:53Z","timestamp":1792999973000,"version":"4.5.0"},"reference-count":44,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["rsnz.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of the Royal Society of New Zealand"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    The bidirectional link between diabetes and cancer is well established, but the role of dietary factors in predicting their comorbidity remains unclear. This study aims to develop a machine learning model to integrate dietary components and to identify key predictors using SHAP analysis.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    Data were derived from the NHANES 2009\u20132012 and 2015\u20132018 cycles, including 20,307 participants (682 with diabetes\u2013cancer comorbidity). After eliminating collinearity and standardizing, 41 dietary features were retained. Seven algorithms\u2014Naive Bayes, support vector machine with radial basis kernel and class weights (SVM\u2010RW), random forest (RF), weighted k\u2010nearest neighbors (weighted k\u2010NN), AdaBoost, LogitBoost, and cancerclass\u2014were trained and evaluated by benchmark tests using AUC\u2010ROC and AUC\u2010PR. The best\u2010performing model was interpreted with SHAP values.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    AdaBoost achieved the highest discrimination, with AUC\u2010ROC of 0.898 and AUC\u2010PR of 0.851. SHAP analysis revealed that vitamins (D, K, A, C, thiamin, total folate, niacin, and \u03b2\u2010carotene), minerals (calcium, iron, sodium, and copper), and carbohydrates\/dietary fiber were the most influential features. Among these, carbohydrates and calcium exerted the dominant negative contribution to comorbidity prediction, indicating that higher intake was associated with a lower predicted probability of diabetes\u2013cancer comorbidity by the model.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    The AdaBoost model exhibited the best performance for predicting diabetes\u2013cancer comorbidity. Dietary components played an important role in disease prediction, among which carbohydrates and calcium served as core negative predictive factors. The identified associations provide clues and evidence for further research on dietary interventions for high\u2010risk populations.\n                  <\/jats:p>","DOI":"10.1002\/snz2.70088","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:17:12Z","timestamp":1791998232000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Study on the Prediction of Diabetes\u2013Cancer Comorbidity Based on Dietary Factors Explained by Seven Machine Learning Algorithms and SHapley Additive exPlanations"],"prefix":"10.1002","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0565-3416","authenticated-orcid":false,"given":"Youcheng","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Bone and Soft Tissue Oncology The First Affiliated Hospital of Guangzhou University of Chinese Medicine  Guangzhou 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