Exploring Sleep Apnea Risk Factors with Contrast Set Mining: Findings from the Sleep Heart Health Study

Sleep apnea is a common sleep disorder with potentially serious health consequences. Identifying risk factors for sleep apnea is crucial for early detection and effective management. Traditionally, this has been achieved through statistical methods such as Pearson’s and Spearman’s correlation analysis, which examine relationships between individual variables and sleep apnea. However, these methods often miss complex, nonlinear patterns and interactions among multiple factors. In this study, we applied contrast set mining to identify patterns in attribute–value pair combinations (contrast sets) in the Sleep Heart Health Study database that differentiate between groups with varying levels of sleep apnea severity. Our findings reveal that males and individuals aged 60 to 80 exhibit a higher risk of sleep apnea, with a confidence exceeding 75%. Moreover, male patients diagnosed with second-degree obesity, defined as a body mass index (BMI) between 35 and 39.9 kg/m2, show an elevated risk of severe apnea, with a lift of over 2.23, support over 16%, and confidence around 80%. In contrast, female patients with a BMI within the normal range (18–25 kg/m2) demonstrate a lower risk of sleep apnea, with a lift of 2.36, support of 17%, and confidence exceeding 90%. Contrast set mining helps uncover meaningful rules within subgroups that traditional methods might overlook. Future research will focus on developing sleep apnea screening models based on these identified contrast set rules.