AUTOMATED EVALUATION OF SUPERVISED LEARNING ALGORITHM FOR ENDOMETRIOSIS PREDICTION

| Keywords and phrases: endometriosis, random forest, decision tree, extreme gradient boosting, logistic regression. Received: January 3, 2023; Accepted: March 17, 2023; Published: May 15, 2023 How to cite this article: S. Visalaxi, T. Sudalaimuthu and K. Hemapriya, Automated evaluation of supervised learning algorithm for endometriosis prediction, JP Journal of Biostatistics 23(2) (2023), 149-172. http://dx.doi.org/10.17654/0973514323009 This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License References: [1] T. Hirata, K. Koga and Y. Osuga, Extra-pelvic endometriosis: a review, Reproductive Medicine and Biology 19(4) (2020), 323-333. [2] N. Marlin, C. Rivas, J. Allotey, J. Dodds, A. Horne and E. Ball, Development and validation of clinical prediction models for surgical success in patients with endometriosis: protocol for a mixed methods study, JMIR Research Protocols 10(4) (2021), e20986. [3] F. Pashizeh, R. Mansouri, F. Davari-Tanha, R. 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