Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women's health. There are many correlated factors associated with chronic pelvic pain (CPP), and analyzing them simultaneously can be complex and involves many challenges. A newly developed interaction ensemble, referred to as INTENSE, was implemented to investigate this research gap. When applied, INTENSE aggregates three machine learning (ML) methods, which are unsupervised, as follows: interaction principal component analysis (IPCA), hierarchical cluster analysis (HCA), and centroid-based clustering (CBC). For our proposed research, we used INTENSE to uncover novel knowledge, which revealed new interactions in a sample of 656 patients among 25 factors: age, parity, ethnicity, body mass index, endometriosis, irritable bowel syndrome, painful bladder syndrome, pelvic floor tenderness, abdominal wall pain, depression score, anxiety score, Pain Catastrophizing Scale, family history of chronic pain, new or re-referral, age when first experienced pain, pain duration, surgery helpful for pain, infertility, smoking, alcohol use, trauma, dysmenorrhea, deep dyspareunia, CPP, and the Endometriosis Health Profile for functional quality of life. INTENSE indicates that CPP and the Endometriosis Health Profile are correlated with depression score, anxiety score, and the Pain Catastrophizing Scale. Other insights derived from these ML methods include the finding that higher body mass index was clustered with smoking and a history of life trauma. As well, sexual pain (deep dyspareunia) was found to be associated with musculoskeletal pain contributors (abdominal wall pain and pelvic floor tenderness). Therefore, INTENSE provided expert-like reasoning without training any model or prior knowledge of CPP. ML has the potential to identify novel relationships in the etiology of CPP, and thus can drive innovative future research.
