Sleep apnea is a critical health issue, and identifying risk factors is essential for effective management. Traditional methods, like Pearson’s and Spearman’s correlations, focus on individual variables but often miss complex, nonlinear interactions. This study applies contrast set mining to uncover patterns in attribute-value pair combinations within the Sleep Heart Health Study dataset. Unlike traditional approaches, this technique identifies relationships among multiple demographic and health factors, offering a more nuanced understanding of sleep apnea risks. These insights pave the way for developing advanced screening models to enhance early detection and management of sleep apnea using information that can easily tracked by wearable devices.