XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women
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 i...
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MDPI AG
2025-06-01
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| Series: | Bioengineering |
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| author | Hojeong Kim Mavlonbek Khomidov Jong-Ha Lee |
| author_facet | Hojeong Kim Mavlonbek Khomidov Jong-Ha Lee |
| author_sort | Hojeong Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9780a933acec46e38734845b802d7bb5 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-9780a933acec46e38734845b802d7bb52025-08-20T02:24:35ZengMDPI AGBioengineering2306-53542025-06-0112665910.3390/bioengineering12060659XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal WomenHojeong Kim0Mavlonbek Khomidov1Jong-Ha Lee2Department of Biomedical Engineering, Keimyung University, Daegu 42601, Republic of KoreaDepartment of Computer Engineering, Keimyung University, Daegu 42601, Republic of KoreaDepartment of Biomedical Engineering, Keimyung University, Daegu 42601, Republic of KoreaIn 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.https://www.mdpi.com/2306-5354/12/6/659hypertensionpostmenopausaleXplainable artificial intelligenceShapley Additive exPlanationsmachine learning |
| spellingShingle | Hojeong Kim Mavlonbek Khomidov Jong-Ha Lee XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women Bioengineering hypertension postmenopausal eXplainable artificial intelligence Shapley Additive exPlanations machine learning |
| title | XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women |
| title_full | XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women |
| title_fullStr | XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women |
| title_full_unstemmed | XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women |
| title_short | XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women |
| title_sort | xgboost and shap based analysis of risk factors for hypertension classification in korean postmenopausal women |
| topic | hypertension postmenopausal eXplainable artificial intelligence Shapley Additive exPlanations machine learning |
| url | https://www.mdpi.com/2306-5354/12/6/659 |
| work_keys_str_mv | AT hojeongkim xgboostandshapbasedanalysisofriskfactorsforhypertensionclassificationinkoreanpostmenopausalwomen AT mavlonbekkhomidov xgboostandshapbasedanalysisofriskfactorsforhypertensionclassificationinkoreanpostmenopausalwomen AT jonghalee xgboostandshapbasedanalysisofriskfactorsforhypertensionclassificationinkoreanpostmenopausalwomen |