AUTHOR=Miao Rujia , Dong Qian , Liu Xuelian , Chen Yingying , Wang Jiangang , Chen Jianwen 

TITLE=A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population

JOURNAL=Frontiers in Public Health

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1365479

DOI=10.3389/fpubh.2024.1365479

ISSN=2296-2565

ABSTRACT=Introduction: An easily-accessible and cost-free model based on machine learning regarding prior probability of vascular aging enables an application to pinpoint high-risk population before physical check, and optimize sanitary investment.A dataset containing questionnaire responses and physical measurement parameters from 77134 adults was extracted from the electronic records of the Health Management Center at the Third Xiangya Hospital. Least absolute shrinkage and selection operator and recursive feature elimination-Lightweight Gradient Elevator were employed to select features from a pool of potential covariates. The participants were randomly divided into a training (70%) and test cohort (30%). Four machine learning algorithms were applied to build the screening models for elevated arterial stiffness (EAS), the performance of models was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy.Results: Fourteen easily accessible features were selected to construct the model, including 'systolic blood pressure'(SBP), 'age', 'waist circumference', 'history of hypertension', 'sex', 'exercise', 'awareness of normal blood pressure', 'eat fruit', 'work intensity', 'drink milk', 'eat bean products', 'smoking', ' alcohol consumption', 'Irritableness'. The Extreme Gradient Boosting (XGBoost) model outperformed the other three models, achieving AUC values of 0.8722 and 0.8710 in the training and test set, respectively. The most important five features are SBP, age, waist, history of hypertension and sex.The XGBoost model ideally assess the prior probability of the current EAS in the general population. The integration of the model into primary care facilities has the potential to lowering medical expanse, and enhance the management of arterial aging.