This retrospective, single-center observational study will use routinely collected perioperative data from adults undergoing surgery for symptomatic hemorrhoidal disease to identify data-driven clinical phenotypes. Unsupervised machine learning will be applied to characterize clusters of patients based on demographic, clinical, anatomical, and surgical variables. The study will explore whether the resulting phenotypes differ in operative complexity and postoperative course, and will generate hypotheses to inform future predictive models and personalized surgical planning.