## 4 Technical Contribution
While the exact values are not that important, the correlation is outlined in color. The interpretation of the matrix is that the darker the color is, the more the data forming those cells are correlated. Given the fact that the dataset was still relatively small, we have applied special techniques to handle it, such as stratified k-fold cross-validation and the ensemble voting before applying the LIME and SHAP explainability models. Stratified k-fold cross-validation is an extension of the cross-validation technique used for classification problems. It maintains the same class ratio throughout the k-folds as the ratio in the original dataset. In our case, since we are dealing with cancer prediction by using a stratified k-fold, the same class ratio is preserved throughout the k-folds. In order to constitute the ensemble, we have created three supervised classification models: a random forest, a support vector machine, and a deep neural network. The random forest model and the SVM both yielded similar results in terms of accuracy, close to 63%, while the neural network reached a 73% percent accuracy after special training configurations. Therefore, it received a higher vote in the overall ensemble accuracy for classification prediction. The random forest was configured with 50 estimators, and the support vector classifier was configured with the parameters obtained by applying GridSearchCV, which performs an exhaustive search over specified parameter values for an estimator. We present in detail the tuning of the neural network classifier. Since we have no possibility of a true validation set, we have used k-folding and various heuristic tests to  
Figure 6: Correlation matrix (first 100 data rows). establish a good configuration of the network. We have pushed the model to overfit in order to see a glimpse of the model capacity, and then we applied L1 regularization, early stopping, a dropout of 0.2 during the training and the corresponding scaling during the test. The final configuration was set with the Adam optimizer, four hidden layers with ReLU activation, and one output layer with sigmoid activation and binary-cross entropy loss. The early stopping trick stopped the network after 29 epochs using a batch size of 16 samples. Given the accuracy of a little over 70%, it is not relevant to display the ROC AUC curve, which represents sensitivity/specificity as a pair corresponding to a particular decision threshold; and instead, we computed both sensitivity and specificity at the standard threshold. We obtained a value of 76.15% for sensitivity and 61.12% for specificity. All of the values were computed based on the confusion matrix. The next step was to apply model-agnostic XAI methods on top of our classifier. We started with the first glimpse of the clinical parameters, as some indicators have greater importance than others. We then focused on some of the most relevant variables, i.e., the presence of nodules rather than age. LIME [4] and SHAP [5] are applicability libraries that can be applied to the output of the network, and they provide global and local coverage, the components that influence most of the inference of the AI model. These results correspond to the correlation matrix, obtained mathematically and independent of the network. The computations were performed in order to see if we achieved similar results with different approaches. In Figure 7, we represent the probability of cancer with red and benign tumors with blue. Figure 7: Cancer incidence probability.