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Published on: May 3, 2018
A curated arterial stiffness dataset for vascular age prediction in China
Xiaohui Chen1,2, Pengcheng Ding3, Mengbo He3
1School of Information Technology, Anqing Vocational and Technical College, Anqing, 246133, China.
Insights
A new dataset of 36,223 participants enables vascular age prediction for cardiovascular health assessment. The Klemera-Doubal Method (KDM) showed the lowest error, while AI models like SVR and XGBoost also performed well.
Area of Science:
- Cardiovascular Health
- Biomarkers
- Aging Research
Background:
- Arterial stiffness is a key indicator of cardiovascular health.
- Vascular age (VA) prediction offers insights beyond chronological age.
- A large dataset is needed for robust VA modeling.
Purpose of the Study:
- To present a curated arterial stiffness dataset from China.
- To benchmark statistical and AI models for VA prediction.
- To provide an open resource for vascular aging research.
Main Methods:
- Dataset compilation: 36,223 participants (30-80 years) from China.
- Model evaluation: Klemera-Doubal Method (KDM) and six AI models (MLR, LASSO, RF, SVR, XGBoost, DNN).
- Performance comparison based on prediction error for VA.
Main Results:
- The dataset supports VA prediction using both statistical and AI approaches.
- KDM demonstrated the lowest prediction error in the benchmark setting.
- Nonlinear AI models, particularly SVR and XGBoost, showed strong performance, outperforming linear models.
Conclusions:
- The presented dataset is a valuable open resource for vascular aging research.
- It facilitates methodological benchmarking for VA prediction models.
- The findings aid in cardiovascular risk assessment through improved VA estimation.
Abstract:
Arterial stiffness is an important biomarker of cardiovascular health, and vascular age (VA) prediction provides additional value beyond chronological age. Here we present a curated arterial stiffness dataset comprising 36,223 participants aged 30-80 years from China. To benchmark its utility for VA modelling, we evaluated the Klemera-Doubal Method (KDM) and six Artificial Intelligence (AI) models: multiple linear regression, LASSO, random forest, support vector regression, XGBoost, and a deep neural network. Results showed that the dataset enables VA prediction using both statistical and learning-based approaches. Across both male and female cohorts, KDM showed the lowest prediction error under the current benchmark setting, while several nonlinear learning-based models achieved better performance than the linear baselines. Among the learning-based methods evaluated here, SVR and XGBoost showed comparatively strong performance. This dataset provides a useful open resource for vascular aging research, cardiovascular risk assessment, and methodological benchmarking.
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