The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES
Abstract Background Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions. The aim of this study was to evaluate the...
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2025-01-01
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| author | Xinghong Guo Mingze Ma Lipei Zhao Jian Wu Yan Lin Fengyi Fei Clifford Silver Tarimo Saiyi Wang Jingyi Zhang Xinya Cheng Beizhu Ye |
| author_facet | Xinghong Guo Mingze Ma Lipei Zhao Jian Wu Yan Lin Fengyi Fei Clifford Silver Tarimo Saiyi Wang Jingyi Zhang Xinya Cheng Beizhu Ye |
| author_sort | Xinghong Guo |
| collection | DOAJ |
| description | Abstract Background Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions. The aim of this study was to evaluate the effectiveness of machine learning-based lifestyle factors in predicting cardiovascular and all-cause mortality and compare the results obtained by traditional methods. Method A prospective cohort study was conducted using a nationally representative sample of adults aged 40 years or older, drawn from the US National Health and Nutrition Examination Survey from 2007 to 2010. The participants underwent a comprehensive in-person interview and medical laboratory examinations, and subsequently, their records were linked with the National Death Index for further analysis. Extreme gradient enhancement, random forest, support vector machine and other machine learning methods are used to build the prediction model. Result Within a cohort comprising 7921 participants, spanning an average follow-up duration of 9.75 years, a total of 1911 deaths, including 585 cardiovascular-related deaths, were recorded. The model predicted mortality with an area under the receiver operating characteristic curve (AUC) of 0.862 and 0.836. Stratifying participants into distinct risk groups based on ML scores proved effective. All lifestyle behaviors were associated with a reduced risk of all-cause and cardiovascular mortality. As age increases, the effects of dietary scores and sedentary time on mortality risk become more pronounced, while the influence of physical activity tends to diminish. Conclusion We develop a ML model based on lifestyle behaviors to predict all-cause and cardiovascular mortality. The developed model offers valuable insights for the assessment of individual lifestyle-related risks. It applies to individuals, healthcare professionals, and policymakers to make informed decisions. |
| format | Article |
| id | doaj-art-53ef0f2216244c708d65bbb29d22bc06 |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-53ef0f2216244c708d65bbb29d22bc062025-01-26T12:56:10ZengBMCBMC Public Health1471-24582025-01-0125111010.1186/s12889-025-21339-wThe association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANESXinghong Guo0Mingze Ma1Lipei Zhao2Jian Wu3Yan Lin4Fengyi Fei5Clifford Silver Tarimo6Saiyi Wang7Jingyi Zhang8Xinya Cheng9Beizhu Ye10Department of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityFaculty of Arts and Social Sciences, Hong Kong Baptist UniversityDepartment of Health Management of Public Health, College of Public Health, Zhengzhou UniversityAbstract Background Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions. The aim of this study was to evaluate the effectiveness of machine learning-based lifestyle factors in predicting cardiovascular and all-cause mortality and compare the results obtained by traditional methods. Method A prospective cohort study was conducted using a nationally representative sample of adults aged 40 years or older, drawn from the US National Health and Nutrition Examination Survey from 2007 to 2010. The participants underwent a comprehensive in-person interview and medical laboratory examinations, and subsequently, their records were linked with the National Death Index for further analysis. Extreme gradient enhancement, random forest, support vector machine and other machine learning methods are used to build the prediction model. Result Within a cohort comprising 7921 participants, spanning an average follow-up duration of 9.75 years, a total of 1911 deaths, including 585 cardiovascular-related deaths, were recorded. The model predicted mortality with an area under the receiver operating characteristic curve (AUC) of 0.862 and 0.836. Stratifying participants into distinct risk groups based on ML scores proved effective. All lifestyle behaviors were associated with a reduced risk of all-cause and cardiovascular mortality. As age increases, the effects of dietary scores and sedentary time on mortality risk become more pronounced, while the influence of physical activity tends to diminish. Conclusion We develop a ML model based on lifestyle behaviors to predict all-cause and cardiovascular mortality. The developed model offers valuable insights for the assessment of individual lifestyle-related risks. It applies to individuals, healthcare professionals, and policymakers to make informed decisions.https://doi.org/10.1186/s12889-025-21339-wCardiovascular mortalityAll-cause mortalityLifestyle behaviorRisk stratificationMortality predictionMachine learning |
| spellingShingle | Xinghong Guo Mingze Ma Lipei Zhao Jian Wu Yan Lin Fengyi Fei Clifford Silver Tarimo Saiyi Wang Jingyi Zhang Xinya Cheng Beizhu Ye The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES BMC Public Health Cardiovascular mortality All-cause mortality Lifestyle behavior Risk stratification Mortality prediction Machine learning |
| title | The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES |
| title_full | The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES |
| title_fullStr | The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES |
| title_full_unstemmed | The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES |
| title_short | The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES |
| title_sort | association of lifestyle with cardiovascular and all cause mortality based on machine learning a prospective study from the nhanes |
| topic | Cardiovascular mortality All-cause mortality Lifestyle behavior Risk stratification Mortality prediction Machine learning |
| url | https://doi.org/10.1186/s12889-025-21339-w |
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