# Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset
## 2 Materials and Methods
### Dataset from Multiple Sample Types
Second, our study was cross-sectional, which means that we did not follow the patients over time to monitor the progression of the disease or the response to treatment. This could limit our understanding of the causal relationships between bacteria and EM disease and the temporal dynamics of surrogate biomarkers. Therefore, we suggest that future studies include larger cohorts, thus increasing the sample size, and conduct longitudinal studies to track changes in the oral sample type over time. Finally, we encourage future studies to explore additional machine learning algorithms or methodologies that may improve the performance or comprehension of our model. This could include fuzzy classifiers, neural networks, ensemble techniques, transfer learning, pre-trained models, or more sophisticated models that could be enhanced by synthetic data. Further investigations using machine learning algorithms could reveal the microbial role in the disease and the local (FRT) and distal (GIT) host-microbial homeostasis related to EM. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Metric** & **Statistic** & **EB** & **EF** & **Vaginal** & **Oral** & **Fecal** & **FRT** & **FRT2** \\ \hline \multirow{4}{*}{Accuracy } & \# & 623 & 349 & 574 & 124 & 261 & 575 & 613 \\  & \(\overline{X}(\%)\) & 67.58 & 25.02 & 54.59 & 76.34 & 66.03 & 62.72 & 63.4 \\  & \(\sigma\) & 0.27 & 0.14 & 0.28 & 0.32 & 0.26 & 0.27 & 0.26 \\ \multirow{2}{*}{Precision } & \(\left\{\begin{array}{c}\overline{X}(\%)\\ \sigma\\ \end{array}\right.\) & 60.54 & 1.72 & 44.37 & 68.82 & 47.19 & 54.84 & 55.85 \\  & \(\sigma\) & 0.34 & 0.07 & 0.37 & 0.4 & 0.39 & 0.36 & 0.32 \\ \multirow{2}{*}{Recall } & \(\left\{\begin{array}{c}\overline{X}(\%)\\ \sigma\\ \end{array}\right.\) & 84.27 & 5.16 & 64.55 & 80.65 & 68.2 & 76.17 & 85.24 \\  & \(\sigma\) & 0.34 & 0.22 & 0.45 & 0.4 & 0.47 & 0.4 & 0.33 \\ \hline \multirow{2}{*}{F1} & \(\left\{\begin{array}{c}\#\\ \overline{X}\left(\%\right)\\ \sigma\\ \end{array}\right.\) & 547 & 18 & 397 & 100 & 178 & 466 & 540 \\  & \(\overline{X}\left(\%\right)\\ \sigma\\ \end{array}\right.\) & 67.05 & 2.58 & 49.56 & 72.45 & 53.96 & 60.23 & 64.13 \\ \multirow{2}{*}{\#} & \(\sigma\) & 0.31 & 0.11 & 0.36 & 0.39 & 0.4 & 0.33 & 0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Global mean (\(\overline{X}\)) and global standard deviation (\(\sigma\)) for scores for SVC classification with rbf kernel, and C = 100. **Author Contributions:** Conceptualization, P.C., L.G.-A., J.A.O. and A.S.-S.; methodology, P.C. and L.G.-A. ; software, P.C. ; validation, P.C. and L.G.-A. ; formal analysis, P.C. and L.G.-A. ; investigation, P.C. ; resources, P.C. and A.S.-S.; data curation, P.C., L.G.-A. and J.A.O. ; writing--original draft preparation, P.C., L.G.-A. and A.S.-S.; writing--review and editing, P.C., L.G.-A., J.A.O. and A.S.-S.; visualization, P.C. ; supervision, L.G.-A. and J.A.O. ; project administration, J.A.O. ; funding acquisition, J.A.O. All authors have read and agreed to the published version of the manuscript. **Funding:** This research has been partially supported by the \"Generation of Reliable Synthetic Health Data for Federated Learning in Secure Data Spaces\" Research Project (PID2022-141045 OB-C42 (AEI/FEDER, UE)) funded by MCIN/AEI/10.13039/501100011033 and by \"ERDF A way of making Europe\" by the \"European Union\". **Institutional Review Board Statement:** The present study was approved by the Ethics Committee of Universidad del Atlantico 1532-N-21 and European University on 22 December 2020. The research was carried out following the guidelines of the Declaration of Helsinki on Medical Research in Human Subjects and Good Clinical Practice. Written informed consent was obtained from all participants. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** Patient data are confidential, so they have been anonymized. They are openly available in figshare at 10.6084/m9.figshare.24125037.