Abstract
Background
It is currently unclear whether machine learning based methods using lifestyle factors can effectively predict the probability of all-cause mortality and cardiovascular disease mortality.
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.
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 0.848 and 0.829. Stratifying participants into distinct risk groups based on ML scores proved effective. All lifestyle behaviors exhibited an inverse association with all-cause and cardiovascular mortality. As age increases, the discernible impacts of dietary scores and sedentary time become increasingly apparent, whereas an opposite trend was observed for physical activity.
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.