{"paper_id":"554b2ec4-d074-4295-ba7a-f7f6a4114e64","body_text":"Abstract\nEndometriosis is a chronic gynecological condition often undiagnosed due to its varied clinical presentations and lack of reliable non-invasive biomarkers. A comparative analysis is performed using machine learning models (MLm) to classify endometriosis using clinical, biomolecular indicators, and hereditary background attributes. Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF), Naive Bayes (NB), Dense Neural Networks (DNN), and XGBoost were compared. Performance is assessed using accuracy, precision, recall, F1-score, and AUROC metrics. Ensemble approaches like Random Forest and XGBoost performed better in terms of accuracy and stability as compared to traditional linear machine learning models. Explainability is achieved using LIME by considering the parameters like pelvic pain, irregular periods, and hereditary attributes. Combining machine learning and explainable AI tools helps in better understanding of the findings and managing such chronic diseases.\nR. Golash, and P. Gaba—These authors contributed equally to this work.\nAccess this chapter\nTax calculation will be finalised at checkout\nPurchases are for personal use only\nSimilar content being viewed by others\nReferences\nSnyder, A., et al.: Preoperative clinical predictors of endometriosis: a machine learning study. J. Minimal. Invasive Gynecol. (2025)\nCao, Y., et al.: Risk factor identification and severity prediction in endometriosis using machine learning. Reprod. Biomed. Online (2025)\nSharma, D.: Endometriosis Clinical Dataset. https://github.com/DeepetSharma/Endometriosis-dataset. Accessed: 2025-09-01 (2023)\nWang, J., et al.: Noninvasive blood-based classifier for differentiating endometriosis and adenomyosis. Front. Digit. Health (2024)\nZaidi, H., et al.: Deep learning for laparoscopic detection of endometriosis lesions: the Glenda dataset study. Comput. Med. Imaging Graphics (2025)\nLiang, J., et al.: A multicenter annotated MRI dataset for endometriosis: segmentation and classification benchmarks. Sci. Data (2025)\nBoye, T., et al.: Explainable neural networks for endometriosis diagnosis using Shap-based interpretability. Artif. Intell. Med. (2025)\nRibeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)\nHosmer, D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression. John Wiley & Sons, ??? (2013)\nCortes, C., Vapnik, V.: Support-vector networks. In: Machine Learning, vol. 20, pp. 273–297. Springer, ??? (1995)\nBreiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)\nChen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)\nLeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)\nAuthor information\nAuthors and Affiliations\nCorresponding author\nEditor information\nEditors and Affiliations\nRights and permissions\nCopyright information\n© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG\nAbout this paper\nCite this paper\nSethi, D., Golash, R., Gaba, P. (2026). Toward Transparent AI in Gynecology: An Endometriosis Classifier with LIME-Based Explanations. In: Kaiser, M.S., Xie, J., Joshi, A. (eds) Intelligent Strategies for ICT. ICTCS 2025. Lecture Notes in Networks and Systems, vol 1894. Springer, Cham. https://doi.org/10.1007/978-3-032-20603-9_8\nDownload citation\nDOI: https://doi.org/10.1007/978-3-032-20603-9_8\nPublished:\nPublisher Name: Springer, Cham\nPrint ISBN: 978-3-032-20602-2\nOnline ISBN: 978-3-032-20603-9\neBook Packages: EngineeringEngineering (R0)Springer Nature Proceedings excluding Computer Science","source_license":"CC0","license_restricted":false}