Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques.

Physical activity (PA) and sleep are associated with cerebrovascular disease and events like stroke. Though the interrelationships between PA, sleep, and other stroke risk factors have been studied, we are unclear about the associations of different types, frequency and duration of PA, sleep behavioral patterns (short, average and long sleep durations), within the context of stroke-related clinical, behavioral, and socio-demographic risk factors. The current study utilized Bayesian Belief Network analysis (BBN), a type of machine learning analysis, to develop profiles of physical activity (duration, intensity, and frequency) and sleep duration associated with or no history of stroke, given the influence of multiple stroke predictors and correlates.

Differential and Combined Effects of Physical Activity Profiles and Prohealth Behaviors on Diabetes Prevalence among Blacks and Whites in the US Population: A Novel Bayesian Belief Network Machine Learning Analysis.

The current study assessed the prevalence of diabetes across four different physical activity lifestyles and infer through machine learning which combinations of physical activity, sleep, stress, and body mass index yield the lowest prevalence of diabetes in Blacks and Whites. Data were extracted from the National Health Interview Survey (NHIS) dataset from 2004-2013 containing demographics, chronic diseases, and sleep duration ( = 288,888). Of the total sample, 9.