Ultrafast pulse wave velocity and ensemble learning to predict atherosclerosis risk

Abstract

Pulse wave velocity (PWV) can evaluate potential atherosclerosis (AS) and ultrafast pulse wave velocity (ufPWV) is a new technique to accurately assess PWV. However, few studies have examined the predictive value of ufPWV for AS risk. We aimed to establish a classification model for AS risk diagnosis based on ufPWV, so that AS can be diagnosed and prevented in advance. We collected imaging data, as well as clinical and laboratory data. A total of 613 patients with 20 attributes were admitted in this study. There were 392 patients with hyperlipidemia (AS risk group) and 221 healthy adults as the control group. In order to build AS risk prediction models, we considered decision tree, five different ensemble learning (EL) models [random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM)] and two different feature selection methods [statistical analysis and RF]. Accuracy and the area under the ROC curve (AUC) were used as the main criterion for model evaluation. In the prediction of AS risk with statistical analysis as the feature selection method, the performances of XGBoost (accuracy: 0.851; AUC: 0.884) and RF (accuracy: 0.844; AUC: 0.889) were better than other models. Besides, in the prediction of AS risk with RF as the feature selection method, the performances of LGBM (accuracy: 0.870; AUC: 0.903) and XGBoost (accuracy: 0.857; AUC: 0.903) were better than other models. In conclusions, EL models with RF as the feature selection method might provide accurate results in predicting AS risk. Besides, ufPWV, especially PWV of left common carotid artery at the end of systole, was an important feature in the AS risk prediction models.

Keywords: Atherosclerosis risk; Ensemble learning; Feature selection; Ultrafast pulse wave velocity.