Key result
An XGBoost machine learning model incorporating lifestyle behaviors and baseline characteristics effectively predicted all-cause and cardiovascular mortality with AUCs of 0.862 and 0.836, respectively.
Why the study?
Although lifestyle and mortality have been explored using traditional methods, machine learning may yield different or more precise predictive conclusions.
Cohort (n=7,921)
Effect estimate: AUC 0.862 (all-cause) and 0.836 (cardiovascular)
A machine learning model incorporating lifestyle behaviors and basic characteristics effectively predicts all-cause and cardiovascular mortality, providing a potential tool for personalized risk assessment.
ML lifestyle scores may refine mortality risk stratification in adults ≥40 years; leaves open whether ML outperforms traditional models in external validation.
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.
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Guo et al. (2025) conducted a cohort in All-cause and cardiovascular mortality (n=7,921). Lifestyle behaviors (diet, physical activity, sedentary time, sleep, smoking, alcohol) was evaluated on Prediction of all-cause and cardiovascular mortality (AUC 0.862 (all-cause) and 0.836 (cardiovascular)). An XGBoost machine learning model incorporating lifestyle behaviors and baseline characteristics effectively predicted all-cause and cardiovascular mortality with AUCs of 0.862 and 0.836, respectively.
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