Data-driven Clustering in Hemorrhoid Surgery: Retrospective Monocentric Study for the Identification of Clinical Phenotypes

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.

Inclusion criteria * Age ≥ 18 years * Clinical and/or intraoperative diagnosis of symptomatic hemorrhoidal disease * Availability of complete perioperative data: demographic, clinical, surgical, and postoperative variables Exclusion criteria * Incomplete or missing clinical data * Presence of anorectal neoplastic conditions (e.g., anal or rectal carcinoma) * Anorectal surgery within the previous 6 months (to avoid confounding effects on symptoms and anatomy)