Model for Endometriosis Detection Using Machine Learning Algorithms

Alexis Bautista, Alfredo Bautista, Jahir Tardillo, José L. Castillo, Lenis Wong
In: 2025 7th International Conference on Software Engineering and Computer Science (CSECS) · 2025 · pp. 1–5 · doi:10.1109/csecs64665.2025.11009460 · W4410855320
article OA: green CC0 ⤵ 1 in-corpus citation
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Abstract

Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four Machine Learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the “endometriosis” class and an overall accuracy of 0.98.

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endometriosisdysmenorrhea

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