{"paper_id":"84c69a90-1429-4d05-bbc1-ef8f5f051ce8","body_text":"A Machine Learning Approach to Predict Endometriosis\nDescription\nAbstarct:\nEndometriosis is a chronic gynecological condition affecting 5-10% of women of reproductive age. Patients with endometriosis often face delays in diagnosis due to non-specific nature of symptoms. This study proposes a predictive model leveraging machine learning to detect endometriosis patients using available electronic health record data from gynecology clinic visits at University of Cincinnati Medical Center. Preliminary results indicated that among seven machine learning algorithms, naïve bayes performed the best with an AUC of 0.79.\nPresented at: AMIA Annual Symposium 2025\nLocation: Atlanta, Georgia, USA\nDates: November 15 to 19, 2025\nAuthors: Parand Shams, MS; Mayur Sarangdhar, PhD; Judith W. Dexheimer, PhD\nAffiliations:\nDepartment of Biostatistics, Health Informatics and Data Science, University of Cincinnati\nDivision of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center\nDepartment of Pediatrics, College of Medicine, University of Cincinnati\nDivision of Oncology, Cincinnati Children’s Hospital Medical Center\nOriginal abstract published in the AMIA proceedings:\nhttps://amia.secure-platform.com/symposium/gallery/rounds/82021/details/19365\nFiles\nP118_Shams.pdf\nFiles\n(687.9 kB)\n| Name | Size | Download all |\n|---|---|---|\n|\nmd5:0b153487ab1a6770de381b991a7d75d5\n|\n687.9 kB | Preview Download |","source_license":"CC0","license_restricted":false}