A study on analyzing risk factors for carotid plaque instability using machine learning models

Objective The objective of this study was to develop and evaluate the effectiveness of machine learning (ML) models in predicting carotid plaque instability. Methods A retrospective analysis was conducted on data from 449 patients with carotid plaques treated at The Affiliated Hospital of Qingdao University between July 2022 and November 2024. Patients were randomly divided into a training set and a testing set at a 7:3 ratio. Four ML algorithms were utilized to establish prediction models. The predictive performance of each model was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was employed to interpret the best-performing model and quantify each feature’s contribution to its predictions. Results Among the four ML algorithms, the light gradient boosting machine (LightGBM) demonstrated the best performance with an AUC of 0.921 (95% confidence interval [CI]: 0.887–0.956) and an accuracy of 0.837 in the training set, and an AUC of 0.864 (95% CI: 0.784–0.944) and an accuracy of 0.811 in the testing set. SHAP values interpreted the contribution of individual features to the model, revealing that C-reactive protein (CRP), leukocytes, high-density lipoprotein (HDL), platelet, neutrophil, lymphocyte-to-monocyte ratio (LMR), fibrinogen, low-density lipoprotein (LDL), and monocyte were the key contributors to the predictive performance. Both the calibration curves and DCA supported the favorable predictive performance of the models. Conclusion Machine learning–based predictive models offer a valuable approach for evaluating the risk of carotid plaque instability by utilizing routinely collected clinical data. These models hold potential as decision-support tools to guide personalized treatment planning. Clinical trial number not applicable.