Affiliation:
1. Shanghai AI Laboratory Shanghai China
2. Department of Endocrinology and Metabolism Peking University People's Hospital Beijing China
3. SenseTime Inc. Beijing China
Abstract
AbstractAims/IntroductionClinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3‐year atherosclerotic cardiovascular disease risk in Chinese type 2 diabetes mellitus patients.Materials and MethodsClinical records of 4,722 individuals with type 2 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model.ResultsAll five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve >0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors.ConclusionsThe prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions.
Subject
General Medicine,Endocrinology, Diabetes and Metabolism,Internal Medicine
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献