{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T04:59:16Z","timestamp":1781240356341,"version":"3.54.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Carotid plaque is a critical risk factor for cardiovascular disease and reflects the extent of the atherosclerotic burden. Compared with non-diabetic individuals, patients with type 2 diabetes mellitus (T2DM) have an elevated likelihood of developing carotid plaques. Consequently, building predictive models tailored to this high-risk population holds significant clinical value for early prevention and management of cardiovascular events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 2,288 patients were included in this study, 1,716 (75.0%) of whom had plaques detected using ultrasound. Baseline data, including demographic characteristics, medical history, and laboratory indicators, were collected, and seven machine-learning algorithms were applied to establish the prediction model. Feature importance was quantified and presented using Shapley Additive Explanations (SHAP). The performance of the model was evaluated using indicators such as area under the curve (AUC-ROC), sensitivity, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study showed that The logistic regression model performed the best in terms of discrimination, calibration, and clinical utility. Through SHAP interpretability analysis, key risk factors such as age, body mass index, glycated hemoglobin, history of hypertension, monocyte count, neutrophil percentage, red blood cell count, sex, estimated glomerular filtration rate, and statin use were identified. This study demonstrated that an effective risk-prediction model can be established using conventional clinical variables and machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed predictive model can help primary care providers detect patients at heightened risk of carotid atherosclerotic plaques, enabling the delivery of targeted preventive strategies and ultimately improving clinical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1801899","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T04:40:40Z","timestamp":1781239240000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a machine learning-based predictive model for carotid plaque in type 2 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