Kim Hojeong, Khomidov Mavlonbek, Lee Jong-Ha
Department of Biomedical Engineering, Keimyung University, Daegu 42601, Republic of Korea.
Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea.
Bioengineering (Basel). 2025 Jun 16;12(6):659. doi: 10.3390/bioengineering12060659.
In postmenopausal women, the prevalence of hypertension increases sharply, emphasizing the importance of its prevention. This increased risk highlights the critical need for effective prevention strategies specifically designed for this population. To address this issue, the present study aimed to identify easily measurable risk factors that contribute to hypertension in postmenopausal women using explainable artificial intelligence (XAI) and machine learning (ML) techniques. This study conducted hypertension classification by analyzing health checkup data from 3289 postmenopausal Korean women aged 55-79 years, extracted from the 2022-2023 Korea National Health Insurance Service (KNHIS) database, using XGBoost, SVM and ANN. XGBoost was the most effective model (AUC: 92.12%, MCC: 0.71) in hypertension classification. Shapley Additive exPlanations-based feature importance identified age and waist circumference (WC) as the most important risk factors for hypertension. In this study, blood pressure increased with variations in WC, a modifiable risk factor. These findings suggest that WC should be managed more strictly to prevent hypertension in postmenopausal women.
在绝经后女性中，高血压患病率急剧上升，凸显了预防高血压的重要性。这种风险的增加突出表明迫切需要专门为这一人群设计的有效预防策略。为解决这一问题，本研究旨在使用可解释人工智能（XAI）和机器学习（ML）技术，识别导致绝经后女性患高血压的易于测量的风险因素。本研究通过分析从2022 - 2023年韩国国民健康保险服务（KNHIS）数据库中提取的3289名年龄在55 - 79岁的韩国绝经后女性的健康检查数据，使用XGBoost、支持向量机（SVM）和人工神经网络（ANN）进行高血压分类。XGBoost是高血压分类中最有效的模型（曲线下面积：92.12%，马修斯相关系数：0.71）。基于夏普利值加法解释的特征重要性分析确定年龄和腰围（WC）是高血压最重要的风险因素。在本研究中，血压随可改变的风险因素腰围的变化而升高。这些发现表明，应更严格地控制腰围，以预防绝经后女性患高血压。
