# Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset
## 2 Materials and Methods
### Dataset from Multiple Sample Types
Furthermore, by taking into account the unbalanced EM class, the results of precision and recall in the oral and fecal regions are very promising. ### Support Vector Classification with the rbf kernel and C = 100

The main parameters for this classification are shown in Table 21. Table 22 summarizes the main findings of this experiment using these parameters. It should be noted that in this case, C was much larger. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Statistic** & **EB** & **EF** & **Vaginal** & **Oral** & **Fecal** & **FRT** & **FRT2** \\ \hline \multirow{4}{*}{Accuracy } & \# & 476 & 491 & 667 & 154 & 278 & 572 & 642 \\  & \(\overline{X}\)(\%) & 38.31 & 35.17 & 25.69 & 79.44 & 69.9 & 47.03 & 44.29 \\  & \(\sigma\) & 0.25 & 0.25 & 0.24 & 0.29 & 0.24 & 0.27 & 0.26 \\ \multirow{2}{*}{Precision } & \(\left\{\begin{array}{c}\overline{X}\)(\%) \\ \sigma\end{array}\right.\) & 21.46 & 20.03 & 18.04 & 70.78 & 53.99 & 35.81 & 33.52 \\  & \(\sigma\) & 0.31 & 0.31 & 0.24 & 0.41 & 0.34 & 0.33 & 0.30 \\ \multirow{2}{*}{Recall } & \(\left\{\begin{array}{c}\overline{X}\)(\%) \\ \sigma\end{array}\right.\) & 34.77 & 30.86 & 36.21 & 79.87 & 81.65 & 59.97 & 60.44 \\  & \(\sigma\) & 0.46 & 0.44 & 0.46 & 0.4 & 0.39 & 0.47 & 0.47 \\ \hline \multirow{2}{*}{F1} & \# & 181 & 169 & 264 & 123 & 227 & 363 & 412 \\  & \(\overline{X}\)(\%) & 25.18 & 22.52 & 22.88 & 73.59 & 62.91 & 42.37 & 40.93 \\ \multirow{2}{*}{Precision } & \(\sigma\) & 0.34 & 0.32 & 0.29 & 0.39 & 0.34 & 0.35 & 0.33 \\ \hline \hline \end{tabular} \# denotes the number of classifiers used in the calculation of the statistics.

\end{table} 
Table 21: The parameters for the support vector classification model (rbf kernel and C = 100). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Value** & **Description** \\ \hline Classifier & SVC & Python classifier from sklearn.svm \\ kernel & rbf & Kernel type to be used \\ gamma & scale & Kernel coefficient \\ C & 100 & Regularization parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 19: The parameters for the support vector classification model (linear kernel). Table 22 shows the results of the SVC classification with the rbf kernel. The F1 score for FRT2 was high, indicating a good performance. The F1 scores for the other regions were similar to those of the other models, except for EF. We have crossed out the F1 score of the EF sample type in Table 22 because the number of classifiers was very small, and it did not produce meaningful statistics. ## 4 Conclusions

This paper presents a comparative analysis of three machine learning classification algorithms: logistic regression, decision trees, and support vector machines. We applied these algorithms to seven sample types to detect EM disease. Our results show that logistic regression outperforms the other two algorithms in terms of the F1 score. The logistic regression achieved an accuracy of 80.07%, a precision of 71.97%, a recall of 81.01%, and an F1 score of 74.77%. Our findings confirm our initial hypothesis that the oral sample type contains relevant information to predict EM disease. They also suggest that a machine learning model based on logistic regression could be a reliable and noninvasive tool for early diagnosis. The findings from our study have several implications for both health and research. They could reduce the costs and risks associated with endometrial analysis and provide insight into surrogate biomarkers associated with EM disease. The machine learning model's results can assist the specialist in considering EM as a potential diagnosis. However, there are some limitations that we faced that future studies should address. First, our sample size was relatively small, with only 12 patients in the oral sample type region and 3 of them with EM disease. Having a small sample size allows for some bias or noise in the data, which could affect the scalability and robustness of our model. Although more information was available for the other sample types, the data were still limited.