Are Combined Risk Factors Linked to In Vitro Fertilization Failure in Polycystic Ovary Syndrome? An Association Rule Mining Approach

Background Polycystic ovary syndrome (PCOS) is a common indication for in vitro fertilization (IVF). Previous studies have often assessed risk factors independently, overlooking the multifactorial interactions that frequently influence clinical pregnancy outcomes. We retrospectively analyzed electronic medical record (EMR) data from PCOS patients who were undergoing IVF. Methods Key clinical variables (age, body mass index [BMI], duration of infertility, hormonal or metabolic disorders, tubal or uterine abnormalities, ovarian conditions including luteinized unruptured follicle syndrome [LUFS], and treatment details) were one-hot-encoded. Apriori association rule mining (ARM) was applied to identify patterns linked to clinical pregnancy failure, using thresholds of support ≥ 0.05, confidence ≥ 0.60, and lift > 1. This approach revealed that multifactorial risk associations were not evident in traditional single-variable analyses. Results The overall clinical pregnancy success rate in the cohort was ~ 40%. ARM revealed several clinically meaningful patterns; notably, maternal age > 35 years frequently appeared in high-risk combinations, often with metabolic or anatomical abnormalities. For instance, the combination of LUFS and tubal obstruction was strongly associated with failure, suggesting a synergistic negative effect. Many of these multifactorial associations may be overlooked using a traditional single-variable analysis approach. Conclusions Apriori rule mining effectively identified complex combinations of risk factors for in vitro fertilization failure in polycystic ovary syndrome, informing individualized strategies. Clinically, identifying advanced-age patients with specific reproductive or metabolic abnormalities may support targeted interventions. This study shows the broader potential of combining ARM with electronic medical record data to reveal hidden patterns for personalized clinical decision-making.