## 3 Results  
The performance of multiple classification models was evaluated using an LOOCV scheme, in combination with different feature selection strategies and data augmentation. Among all tested configurations, the best-performing models were selected based on the maximum AUC of the ROC curve. Table 1 summarises the results obtained with the best-performing classifiers: KNN, RF, SVM, and XGBoost. The ROC curves corresponding to the two best-performing configurations are shown in Figure 3, while Figure 4 shows an example distribution of the prediction scores obtained for each sample during LOOCV with the RF model (with the same parameters in Table 1), grouped by class. This plot allows for a visual comparison of the model output between the two classes and provides insight into the variability of the predicted scores across folds. Figure 2: Flow chart of the study. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Model** & **Hyper Parameters** & **Augmentation** & \begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Selected** \\ **Features** \\ \end{tabular} & **AUC** & \begin{tabular}{c} **Accuracy** \\ **(2x)** \\ \end{tabular} & \begin{tabular}{c} **Threshold** \\ **(Best-Accuracy** \\ **Foin)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Confusion** \\ **Matrix** \\ \end{tabular} \\ \hline KNN & n\_neighbors = 5 & & & & & & & & & & & \\ weights = uniform & No & MEMR & 13 & 0.78 & 0.76 & 0.80 & 0.77 & 0.42 & 0.54 & [44] \\ metric = cosine & & & & & & & & & & & \\ \hline objective = binarylogistic & & & & & & & & & & & & \\ max\_depth = 6 & & & & & & & & & & & & \\ learning\_rate = 0 & & & & & & & & & & & & \\ XGBout & n\_estimators = 300 lambda = 0.5 & No & Mutual Info & 4 & 0.77 & 0.76 & 0.53 & 0.73 & 0.46 & 0.56 & [44] \\ alpha = 0.5 & & & & & & & & & & & \\ eval\_metric = aac & & & & & & & & & & & \\ scale\_pos\_weight = 0.5435 & & & & & & & & & & & \\ \hline n\_estimators = 200 & & & & & & & & & & & & \\ criterion & g\(\equiv\) min\_score\_split = 5 & & & & & & & & & & & \\ min\_samples\_leaf = 2 & & & & & & & & & & & \\ random\_state = 42 & & & & & & & & & & & \\ \hline C = 5 & & & & & & & & & & & & & \\ kernel = RF\_gamma = scale & & & & & & & & & & & \\ shrinking = True & & & & & & & & & & & & \\ probability = & & & & & & & & & & & & \\ True class\_weight = balanced & & & & & & & & & & & \\ random\_state = 42 & & & & & & & & & & & \\ \hline objective = binarylogistic & & & & & & & & & & & & \\ max\_depth = 6 & & & & & & & & & & & & \\ learning\_rate = 0.01 & & & & & & & & & & & & \\ xGboost & n estimators = 300 lambda = 0.5 & & Yes & MEMR & 6 & 0.79 & 0.77 & 0.80 & 0.67 & 0.67 & [44] \\ alpha = 0.5 & & & & & & & & & & & \\ eval\_metric = aac & & & & & & & & & & & \\ scale\_pos\_weight = 0.5435 & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the best-performing Machine Learning classifiers in terms of classification performance for distinguishing in situ from invasive breast cancer lesions. Metrics include accuracy, precision, recall, and F1-score calculated at the best-accuracy point. Figure 4: Boxplot of the prediction scores obtained in LOOCV with a Random Forest model, shown separately for the positive and negative classes. Each included value represents the score assigned to a sample when it was used as the validation case. This visualisation highlights the distribution of model outputs across classes. Figure 3: Receiver Operating Characteristic (ROC) curves for the best-performing classifiers. The blue curve represents the K-Nearest Neighbors (KNN) model trained on non-augmented data using the Minimum Redundancy Maximum Relevance (MRMR)-selected features. The orange curve corresponds to the XGBoost model trained on non-augmented data with mutual information-based feature selection.