Predicting Carotid Atherosclerosis in Latent Autoimmune Diabetes in Adult Patients Using Machine Learning Models: A Retrospective Study

Background: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an elevated risk of developing cardiovascular diseases, including carotid atherosclerosis. While machine learning models have been widely used in predicting cardiovascular risks in Type 1 and Type 2 diabetes, research on LADA remains limited. Early prediction of carotid atherosclerosis using machine learning models could help in timely intervention and improved patient outcomes for this specific population. Methods: We conducted a retrospective cross-sectional analysis involving 142 LADA patients diagnosed within the endocrinology department at Shanxi Bethune Hospital, China. Various clinical, demographic, and laboratory variables were analyzed using univariate and multivariate logistic regression, complemented by LASSO regression for feature selection. Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis. Results: Significant risk factors for carotid atherosclerosis were identified, including age, smoking history, BMI, ALB, HDL-C, and ALT. Among the various machine learning models evaluated, the LR model exhibited the highest performance, achieving an area under the curve (AUC) of 0.936, alongside an accuracy of 86%. NNET and SVM models also demonstrated robust predictive capacities with AUC values of 0.919 and 0.918, respectively. Conclusions: This study highlights the critical role of identifying risk factors for carotid atherosclerosis in LADA patients. Our use of ML models builds on the growing body of work in diabetes-related cardiovascular risk prediction, and it offers a novel approach by specifically targeting the LADA population. Incorporating ML models into clinical practice could improve risk stratification and patient management in LADA. Future research should validate these models across diverse populations and investigate the underlying mechanisms linking LADA to cardiovascular risk.