## 2 Materials and Methods
### Evaluation of Results  
The next stage of this study is to evaluate the prediction results of each prediction model in order to eventually discern the optimal one. To this end, six widely implemented metric scores were used: accuracy [38], sensitivity [39], specificity [39], Jaccard score [40], F1-score [41], and confusion matrix [42]. All of the above metrics determine the performance of a model by using a combination of correlation between true positive (TP), true negative (TN), false positive (FP), and false negative (FN) instances. Traditionally, the most important metric in prediction studies is considered to be accuracy. Still, this cannot be the case in this particular dataset. This being a highly imbalanced dataset, basing the selection of the optimal model mainly on accuracy can be deceiving [43; 44; 45]. Due to the fact that one class is considerably more prevalent, a prediction model could be trained to predict the prevalent class very well, while the other(s) are not predicted well. This shortcoming, nevertheless, is not accurately depicted in the overall accuracy of the model. Therefore, when dealing with imbalanced datasets, the most important features tend to be the confusion matrix, sensitivity/specificity for each class, and the receiver operating characteristic (ROC) curve [46]. It is possible that oversampling techniques may result in a lesser overall accuracy for the above reasons. They do, however, create a more all-around trained estimator that is eventually more suitable to classify every instance instead of just the prevalent class. Specificity and sensitivity are two important measures that help evaluate the performance of a diagnostic test. Sensitivity is the proportion of true positives that are correctly identified by the test, while specificity is the proportion of true negatives that are correctly identified by the test. These measures are important when evaluating the effectiveness of a medical test or a decision-making tool. This can be especially true for classification scenarios of two classes in total, such as the current one (adenoma/MG classification). These metrics can provide insight into the model's ability to correctly identify individuals who belong to the first class (sensitivity; in the current scenario this is for adenoma) and those who do not (specificity). The mathematical formulas used to calculate the aforementioned metrics are expressed as follows:  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{1}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{2}\]  
On the other hand, the ROC curve is a useful graphical tool employed to evaluate the performance of a binary classification model. It plots the true positive rate (sensitivity) against the false positive rate (specificity) for various threshold values, allowing the user to select a threshold that balances the trade-off between sensitivity and specificity. The area under the ROC curve (AUC) is a commonly used metric to quantify the overall performance of a classification model. A perfect model will have an AUC of 1, while a random model will have an AUC of 0.5 [47]. Lastly, a confusion matrix is a table that summarizes the performance of a classification model by comparing predicted and actual outcomes. In binary classification problems, such as in this work, it is a 2 by 2 matrix that is formed by the TP, TN, FP, and FN fields. Such a confusion matrix has the following form: \begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{Predicted condition} \\ \cline{3-4}  & & Positive (PP) & Negative (PN) \\ \hline \multirow{2}{*}{Actual condition} & Positive (P) & True positive (TP) & False negative (FN) \\ \cline{2-4}  & Negative (N) & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}   
In order to further validate the performance of each tested prediction model, a 10-fold stratified cross validation was employed. Hence, these cross-validated results were used tocalculate the performance of each ML model.