LAUSR

The purpose of this study was to confirm the potential of XGBoost as a vascular aging assessment model based on the photoplethysmogram (PPG) features suggested in previous studies, and to explore the key PPG features for vascular aging assessment through an explainable artificial intelligence method. The PPG waveforms obtained from 752 volunteers aged 19–87 years were analyzed and a total of 78 features were derived that were proposed in previous studies. Age was estimated through an XGBoost regression model, and estimation error was calculated in terms of mean absolute error and root-mean-squared error. To evaluate feature importance, gain, coverage, weight, and SHAP value was calculated. The vascular aging assessment model developed using XGBoost has 8.1 years of mean-absolute error and 9.9 years of root-mean-squared error, a correlation coefficient of 0.63 with actual age, and a coefficient of determination of 0.39. Feature importance analysis using the SHAP value confirmed that features, such as systolic and diastolic peak amplitude, risetime, skewness, and pulse area, play a key role in vascular aging assessment. The XGBoost regression model showed an equal level of performance to the existing PPG-based vascular aging assessment models. Moreover, the result of feature importance analysis using explainable artificial intelligence verified that the features proposed in previous vascular aging assessment studies, such as reflective index and risetime, were more important in vascular aging assessment than other PPG features.