# Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset
## 1 Introduction  
Endometriosis (EM) is a chronic inflammatory estrogen-dependent disorder characterized by endometrial-like tissue outside the uterus [1]. It affects 10% of women worldwide, causing pelvic pain and infertility [2]. The uterus is not a sterile organ [3]. The inner lining of the uterus, the endometrium, contains resident microorganisms that are different in type and number from the microorganisms that reside in the vagina [4]. A conclusive clinical diagnosis of EM usually requires a combination of medical history, physical examination, imaging techniques (such as ultrasound or MRI), and sometimes laparoscopic surgery. Currently, no single definitive biomarker can diagnose EM with 100% accuracy. Recent studies show that the microbial composition in EM differs from that in healthy individuals. This implies that it plays a significant role in disease and reproductive outcomes [5]. The composition and diversity of the microbiota [6] can indicate the onset or progression of the disease. Distal microbial changes can serve as biomarkers for EM. Gastrointestinal biomarkers, such as those taken from the intestinal tract or oral cavity, are quantifiable indicators that can offer valuable information on overall health. Using samples from the oral microbiome could have advantages over other internal body sites in the female reproductive tract because the process for collection is noninvasive, and specialized samplecollection procedures are not required for the evaluation of EM. Consequently, the early detection of EM can help avoid complications such as reduced fertility. Therefore, the timely diagnosis of this condition is crucial for effective treatment and for the preservation of the patient's reproductive health. Machine learning algorithms have proven their effectiveness in solving classification problems [7]. These algorithms can employ methods such as logistic regression, decision trees, SVMs, neural networks, and fuzzy classifiers, among others [8; 9]. Machine learning has been applied to various problems in the domain of endometrial disease detection [10; 11; 12]. For example, some researchers have proposed a novel machine learning algorithm to create precision prognostication systems for endometrial cancer [13; 14], while others have used deep learning to classify MRI images of endometrial cancer [15]. These studies demonstrate the potential of machine learning to improve the diagnosis and treatment of endometrial diseases. Another study using noninvasive biomarkers [16], such as blood, urine, or endometrial biopsy, was carried out using the QUADAS-2 tool as an example of a failed attempt, and the conclusion was that they could not replace laparoscopy as a diagnostic procedure. It is important to note that while these biomarkers are promising, none of them are currently used as an independent diagnostic tool for EM [17]. Data scarcity is a common challenge for all algorithms, not only for those used for classification purposes. This problem limits AI applications in the real world, especially in medicine, where data are costly and public funding is needed [18]. Moreover, the availability of public records is limited [19]. In these cases, algorithms fail to achieve an adequate generalization capacity, which results in poor performance. Another major problem facing classification learning algorithms is the imbalance between classes in datasets [20]. That is, one or more classes have most of the examples, while the remaining classes are underrepresented. The problem becomes more severe when the class with the least amount of data is the most relevant [21]. In this study, several machine learning algorithms have been considered in order to provide a classification of EM disease from the microbiome. Our hypothesis asserts that the bacterial taxa present in the oral or fecal microbiome could serve as a surrogate biomarker for the diagnosis of EM in women.