Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS)
Abstract Background Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD...
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | Global Health Research and Policy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41256-022-00282-y |
| _version_ | 1811177910593126400 |
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| author | Jia-Xin Li Li Li Xuemei Zhong Shu-Jun Fan Tao Cen Jianquan Wang Chuanjiang He Zhoubin Zhang Ya-Na Luo Xiao-Xuan Liu Li-Xin Hu Yi-Dan Zhang Hui-Ling Qiu Guang-Hui Dong Xiao-Guang Zou Bo-Yi Yang |
| author_facet | Jia-Xin Li Li Li Xuemei Zhong Shu-Jun Fan Tao Cen Jianquan Wang Chuanjiang He Zhoubin Zhang Ya-Na Luo Xiao-Xuan Liu Li-Xin Hu Yi-Dan Zhang Hui-Ling Qiu Guang-Hui Dong Xiao-Guang Zou Bo-Yi Yang |
| author_sort | Jia-Xin Li |
| collection | DOAJ |
| description | Abstract Background Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region. Methods A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods—Random Forest, Random Ferns, and Extreme Gradient Boosting—to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking. Results The prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample. Conclusions CVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future. |
| first_indexed | 2024-04-11T06:09:16Z |
| format | Article |
| id | doaj.art-fd669cd206984d568feba42d1763ff0c |
| institution | Directory Open Access Journal |
| issn | 2397-0642 |
| language | English |
| last_indexed | 2024-04-11T06:09:16Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Global Health Research and Policy |
| spelling | doaj.art-fd669cd206984d568feba42d1763ff0c2022-12-22T04:41:22ZengBMCGlobal Health Research and Policy2397-06422022-12-017111310.1186/s41256-022-00282-yMachine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS)Jia-Xin Li0Li Li1Xuemei Zhong2Shu-Jun Fan3Tao Cen4Jianquan Wang5Chuanjiang He6Zhoubin Zhang7Ya-Na Luo8Xiao-Xuan Liu9Li-Xin Hu10Yi-Dan Zhang11Hui-Ling Qiu12Guang-Hui Dong13Xiao-Guang Zou14Bo-Yi Yang15Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University)Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University)Guangzhou Center for Disease Control and PreventionDepartment of Research and Development, Nanfang Hospital, Southern Medical UniversityDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University)Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University)Guangzhou Center for Disease Control and PreventionGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University)Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen UniversityAbstract Background Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region. Methods A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods—Random Forest, Random Ferns, and Extreme Gradient Boosting—to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking. Results The prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample. Conclusions CVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future.https://doi.org/10.1186/s41256-022-00282-yCardiovascular disease (CVD)PredictionProminent factorsMachine learningKashgar prefecture |
| spellingShingle | Jia-Xin Li Li Li Xuemei Zhong Shu-Jun Fan Tao Cen Jianquan Wang Chuanjiang He Zhoubin Zhang Ya-Na Luo Xiao-Xuan Liu Li-Xin Hu Yi-Dan Zhang Hui-Ling Qiu Guang-Hui Dong Xiao-Guang Zou Bo-Yi Yang Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS) Global Health Research and Policy Cardiovascular disease (CVD) Prediction Prominent factors Machine learning Kashgar prefecture |
| title | Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS) |
| title_full | Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS) |
| title_fullStr | Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS) |
| title_full_unstemmed | Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS) |
| title_short | Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS) |
| title_sort | machine learning identifies prominent factors associated with cardiovascular disease findings from two million adults in the kashgar prospective cohort study kpcs |
| topic | Cardiovascular disease (CVD) Prediction Prominent factors Machine learning Kashgar prefecture |
| url | https://doi.org/10.1186/s41256-022-00282-y |
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