Development and validation of a carotid plaque risk prediction model for coal miners
ObjectiveCarotid plaque represents an independent risk factor for cardiovascular disease and a significant threat to human health. The aim of the study is to develop an accurate and interpretable predictive model for early detection the occurrence of carotid plaque.MethodsA cross-sectional study was...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1490961/full |
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| author | Yi-Chun Li Yi-Chun Li Yi-Chun Li Tie-Ru Zhang Tie-Ru Zhang Tie-Ru Zhang Fan Zhang Fan Zhang Fan Zhang Chao-Qun Cui Chao-Qun Cui Chao-Qun Cui Yu-Tong Yang Yu-Tong Yang Yu-Tong Yang Jian-Guang Hao Jian-Ru Wang Jiao Wu Hai-Wang Gao Ying-Bo Liu Ming-Zhong Luo Li-Jian Lei Li-Jian Lei Li-Jian Lei |
| author_facet | Yi-Chun Li Yi-Chun Li Yi-Chun Li Tie-Ru Zhang Tie-Ru Zhang Tie-Ru Zhang Fan Zhang Fan Zhang Fan Zhang Chao-Qun Cui Chao-Qun Cui Chao-Qun Cui Yu-Tong Yang Yu-Tong Yang Yu-Tong Yang Jian-Guang Hao Jian-Ru Wang Jiao Wu Hai-Wang Gao Ying-Bo Liu Ming-Zhong Luo Li-Jian Lei Li-Jian Lei Li-Jian Lei |
| author_sort | Yi-Chun Li |
| collection | DOAJ |
| description | ObjectiveCarotid plaque represents an independent risk factor for cardiovascular disease and a significant threat to human health. The aim of the study is to develop an accurate and interpretable predictive model for early detection the occurrence of carotid plaque.MethodsA cross-sectional study was conducted by selecting coal miners who participated in medical examinations from October 2021 to January 2022 at a hospital in North China. The features were initially screened using extreme gradient boosting (XGBoost), random forest, and LASSO regression, and the model was subsequently constructed using logistic regression. The three models were then compared, and the optimum model was identified. Finally, a nomogram was plotted to increase its interpretability.ResultsThe XGBoost algorithm demonstrated superior performance in feature screening, identifying the top five features as follows: age, systolic blood pressure, low-density lipoprotein cholesterol, white blood cell count, and body mass index (BMI). The area under the curve (AUC), sensitivity, and specificity of the model constructed based on the XGBoost algorithm were 0.846, 0.867, and 0.702, respectively.ConclusionsIt is possible to predict the presence of carotid plaque using machine learning. The model has high application value and can better predict the risk of carotid artery plaque in coal miners. Furthermore, it provides a theoretical basis for the health management of coal miners. |
| format | Article |
| id | doaj-art-ea7f1f8e11f64b0abc776cfcf081aacd |
| institution | DOAJ |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-ea7f1f8e11f64b0abc776cfcf081aacd2025-08-20T02:56:12ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-05-011210.3389/fcvm.2025.14909611490961Development and validation of a carotid plaque risk prediction model for coal minersYi-Chun Li0Yi-Chun Li1Yi-Chun Li2Tie-Ru Zhang3Tie-Ru Zhang4Tie-Ru Zhang5Fan Zhang6Fan Zhang7Fan Zhang8Chao-Qun Cui9Chao-Qun Cui10Chao-Qun Cui11Yu-Tong Yang12Yu-Tong Yang13Yu-Tong Yang14Jian-Guang Hao15Jian-Ru Wang16Jiao Wu17Hai-Wang Gao18Ying-Bo Liu19Ming-Zhong Luo20Li-Jian Lei21Li-Jian Lei22Li-Jian Lei23Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, ChinaResearch Centre of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, ChinaResearch Centre of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, ChinaResearch Centre of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, ChinaResearch Centre of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, ChinaResearch Centre of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Occupational Diseases and Poisoning, The Second People’s Hospital of Shanxi Province, Taiyuan, ChinaDepartment of Medical and Education, The Second People’s Hospital of Shanxi Province, Taiyuan, ChinaDepartment of Medical and Education, The Second People’s Hospital of Shanxi Province, Taiyuan, ChinaPeking University Medical Lu'an Hospital Health Management Center, Changzhi, Shanxi, ChinaPeking University Medical Lu'an Hospital Health Management Center, Changzhi, Shanxi, ChinaOffice of the President, The Second People’s Hospital of Shanxi Province, Taiyuan, ChinaDepartment of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, ChinaResearch Centre of Environmental Pollution and Major Chronic Diseases Epidemiology, Shanxi Medical University, Taiyuan, Shanxi, ChinaObjectiveCarotid plaque represents an independent risk factor for cardiovascular disease and a significant threat to human health. The aim of the study is to develop an accurate and interpretable predictive model for early detection the occurrence of carotid plaque.MethodsA cross-sectional study was conducted by selecting coal miners who participated in medical examinations from October 2021 to January 2022 at a hospital in North China. The features were initially screened using extreme gradient boosting (XGBoost), random forest, and LASSO regression, and the model was subsequently constructed using logistic regression. The three models were then compared, and the optimum model was identified. Finally, a nomogram was plotted to increase its interpretability.ResultsThe XGBoost algorithm demonstrated superior performance in feature screening, identifying the top five features as follows: age, systolic blood pressure, low-density lipoprotein cholesterol, white blood cell count, and body mass index (BMI). The area under the curve (AUC), sensitivity, and specificity of the model constructed based on the XGBoost algorithm were 0.846, 0.867, and 0.702, respectively.ConclusionsIt is possible to predict the presence of carotid plaque using machine learning. The model has high application value and can better predict the risk of carotid artery plaque in coal miners. Furthermore, it provides a theoretical basis for the health management of coal miners.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1490961/fullXGBoostnomogrammachine learningcoal minerscarotid plaque |
| spellingShingle | Yi-Chun Li Yi-Chun Li Yi-Chun Li Tie-Ru Zhang Tie-Ru Zhang Tie-Ru Zhang Fan Zhang Fan Zhang Fan Zhang Chao-Qun Cui Chao-Qun Cui Chao-Qun Cui Yu-Tong Yang Yu-Tong Yang Yu-Tong Yang Jian-Guang Hao Jian-Ru Wang Jiao Wu Hai-Wang Gao Ying-Bo Liu Ming-Zhong Luo Li-Jian Lei Li-Jian Lei Li-Jian Lei Development and validation of a carotid plaque risk prediction model for coal miners Frontiers in Cardiovascular Medicine XGBoost nomogram machine learning coal miners carotid plaque |
| title | Development and validation of a carotid plaque risk prediction model for coal miners |
| title_full | Development and validation of a carotid plaque risk prediction model for coal miners |
| title_fullStr | Development and validation of a carotid plaque risk prediction model for coal miners |
| title_full_unstemmed | Development and validation of a carotid plaque risk prediction model for coal miners |
| title_short | Development and validation of a carotid plaque risk prediction model for coal miners |
| title_sort | development and validation of a carotid plaque risk prediction model for coal miners |
| topic | XGBoost nomogram machine learning coal miners carotid plaque |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1490961/full |
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