Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset

Pablo Caballero, Luis González Abril, Juan Antonio Ortega, Áurea Simón‐Soro
In: Algorithms · 2024 · vol. 17(3) , pp. 108 · doi:10.3390/a17030108 · W4392469894
article OA: gold CC0 ⤵ 2 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-24 ⓘ

This pilot study evaluated machine learning algorithms for endometriosis detection using microbiome data from five sample types, achieving high performance and suggesting potential for less invasive diagnostic procedures in low-resource settings.

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Abstract

Endometriosis (EM) is a chronic inflammatory estrogen-dependent disorder that affects 10% of women worldwide. It affects the female reproductive tract and its resident microbiota, as well as distal body sites that can serve as surrogate markers of EM. Currently, no single definitive biomarker can diagnose EM. For this pilot study, we analyzed a cohort of 21 patients with endometriosis and infertility-associated conditions. A microbiome dataset was created using five sample types taken from the reproductive and gastrointestinal tracts of each patient. We evaluated several machine learning algorithms for EM detection using these features. The characteristics of the dataset were derived from endometrial biopsy, endometrial fluid, vaginal, oral, and fecal samples. Despite limited data, the algorithms demonstrated high performance with respect to the F1 score. In addition, they suggested that disease diagnosis could potentially be improved by using less medically invasive procedures. Overall, the results indicate that machine learning algorithms can be useful tools for diagnosing endometriosis in low-resource settings where data availability and availability are limited. We recommend that future studies explore the complexities of the EM disorder using artificial intelligence and prediction modeling to further define the characteristics of the endometriosis phenotype.

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Condition tags

endometriosisinfertility

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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