Guo Xinghong, Ma Mingze, Zhao Lipei, Wu Jian, Lin Yan, Fei Fengyi, Tarimo Clifford Silver, Wang Saiyi, Zhang Jingyi, Cheng Xinya, Ye Beizhu
Department of Health Management of Public Health, College of Public Health, Zhengzhou University, 100 Kexue Road, Gaoxin district, Zhengzhou, 450001, Henan, China.
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China.
BMC Public Health. 2025 Jan 24;25(1):319. doi: 10.1186/s12889-025-21339-w.
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.
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.
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.
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.
传统方法已对生活方式与心血管死亡率及全因死亡率进行了详尽研究，但机器学习（ML）相较于传统方法的优势可能会得出不同或更精确的结论。本研究旨在评估基于机器学习的生活方式因素在预测心血管和全因死亡率方面的有效性，并比较传统方法所得结果。
使用2007年至2010年美国国家健康与营养检查调查中具有全国代表性的40岁及以上成年人样本进行前瞻性队列研究。参与者接受了全面的面对面访谈和医学实验室检查，随后，他们的记录与国家死亡指数相链接以进行进一步分析。使用极端梯度提升、随机森林、支持向量机等机器学习方法构建预测模型。
在一个包含7921名参与者、平均随访时长为9.75年的队列中，共记录了1911例死亡，其中包括585例心血管相关死亡。该模型预测死亡率的受试者工作特征曲线下面积（AUC）为0.862和0.836。根据ML评分将参与者分为不同风险组被证明是有效的。所有生活方式行为都与全因和心血管死亡率风险降低相关。随着年龄增长，饮食评分和久坐时间对死亡风险的影响变得更加显著，而身体活动的影响则趋于减弱。
我们开发了一种基于生活方式行为的ML模型来预测全因和心血管死亡率。所开发的模型为评估个体生活方式相关风险提供了有价值的见解。它适用于个人、医疗保健专业人员和政策制定者做出明智决策。
