Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning - Li, Qingqing; Xie, Wenhui; Li, Liping; Wang, Lijing; You, Qinyi; Chen, Lu; Li, Jing; Ke, Yilang; Fang, Jun; Liu, Libin; Hong, Huashan (Frontiers in Physiology, 2021)
Background: Arterial stiffness assessed by pulse wave velocity is a major risk factor for cardiovascular diseases. The incidence of cardiovascular events remains high in diabetics. However, the clinical prediction model for elevated arterial stiffness using machine learning to identify subjects consequently at higher risk remains to be developed. Methods: Least absolute shrinkage and selection operator and support vector machine-recursive feature elimination were used for feature selection. Four machine learning algorithms were used to construct a prediction model, and their performance was compared based on an area under the receiver operating characteristic curve metric in a discovery dataset from (n = 760). The model with the best performance was selected and validated in an independent dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). To apply our model to clinical practice, we built a free and user-friendly web online tool. Results: The predictive model includes the predictors age, systolic pressure blood, diastolic pressure blood, and body mass index. In the discovery cohort, the gradient boosting-based model outperformed other methods in the elevated arterial stiffness prediction. In the validation cohort, the gradient boosting model showed a good discrimination capacity. A cutoff value of 0.46 of the elevated arterial stiffness risk score in the gradient boosting model resulted in a good specificity (0.813 in the discovery data and 0.761 in the validation data) and sensitivity (0.875 and 0.738, respectively) trade-off points. Conclusions: The gradient boosting-based prediction system presents a good classification in the elevated arterial stiffness prediction. The web online tool makes our gradient boosting-based model easily accessible for further clinical studies and utilization.