A Machine Learning Approach to Predict Endometriosis

Parand Shams, Mayur Sarangdhar, Judith W. Dexheimer
2025 · doi:10.5281/zenodo.19472254 · W7151797317
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A machine learning study using electronic health records from gynecology clinic visits identified naïve Bayes as the optimal algorithm for predicting endometriosis, achieving an AUC of 0.79.

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This study addresses the diagnostic delays associated with endometriosis by developing a machine learning model to identify patients using electronic health record data from gynecology clinic visits. The researchers evaluated seven different algorithms, finding that the naïve Bayes classifier achieved the highest performance with an area under the curve of 0.79. These preliminary results demonstrate the potential for leveraging clinical data to improve early detection rates for this chronic condition. This paper is centrally about endometriosis — specifically utilizing predictive modeling to aid in its diagnosis based on patient visit records.

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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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