A Machine Learning Approach to Predict Endometriosis

Parand Shams, Mayur Sarangdhar, Judith W. Dexheimer
2025 · doi:10.5281/zenodo.19472253 · W7151935832
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This study developed a machine learning model using electronic health record data to predict endometriosis, finding that naïve Bayes achieved the highest performance with an AUC of 0.79 among seven algorithms.

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This study addresses the diagnostic delays associated with endometriosis by developing a predictive model using machine learning algorithms on electronic health record data from gynecology clinic visits at the University of Cincinnati Medical Center. The researchers evaluated seven different algorithms to determine which could most effectively identify patients with the condition based on available clinical data. Preliminary results indicated that the naïve Bayes algorithm achieved the highest performance, yielding an area under the curve (AUC) of 0.79. This paper is centrally about endometriosis — specifically utilizing machine learning to improve early detection and reduce diagnostic latency through electronic health record analysis.

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Abstract

Abstarct: Endometriosis 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. Presented at: AMIA Annual Symposium 2025Location: Atlanta, Georgia, USADates: November 15 to 19, 2025 Authors: Parand Shams, MS; Mayur Sarangdhar, PhD; Judith W. Dexheimer, PhD Affiliations: Department of Biostatistics, Health Informatics and Data Science, University of CincinnatiDivision of Biomedical Informatics, Cincinnati Children’s Hospital Medical CenterDepartment of Pediatrics, College of Medicine, University of CincinnatiDivision of Oncology, Cincinnati Children’s Hospital Medical Center Original abstract published in the AMIA proceedings: https://amia.secure-platform.com/symposium/gallery/rounds/82021/details/19365
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A Machine Learning Approach to Predict Endometriosis Description Abstarct: Endometriosis 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. Presented at: AMIA Annual Symposium 2025 Location: Atlanta, Georgia, USA Dates: November 15 to 19, 2025 Authors: Parand Shams, MS; Mayur Sarangdhar, PhD; Judith W. Dexheimer, PhD Affiliations: Department of Biostatistics, Health Informatics and Data Science, University of Cincinnati Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center Department of Pediatrics, College of Medicine, University of Cincinnati Division of Oncology, Cincinnati Children’s Hospital Medical Center Original abstract published in the AMIA proceedings: https://amia.secure-platform.com/symposium/gallery/rounds/82021/details/19365 Files P118_Shams.pdf Files (687.9 kB) | Name | Size | Download all | |---|---|---| | md5:0b153487ab1a6770de381b991a7d75d5 | 687.9 kB | Preview Download |

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