## 4 Discussion  
The driving objective behind this work is multifaceted. First of all, an accurate computer-aided decision-making system was to be generated that could predict all the classes of the particular imbalanced dataset equally accurately. Tables 2 and 3 and Figure 2 highlight how, after being trained on an oversampled set, each prediction model demonstrated a more balanced prediction accuracy for both classes. Furthermore, the next goal of this work was to shed light on the decision-making process of such a black-box computer-aided decision-making model. On this scope, Figures 3 and 4 offer valuable insight as to which features were the most essential for the classification and how each datum contributed to the final outcome. Below, we will try to analyze the findings more analytically, explain what these plots showcase, and finally comment on the findings from a medical viewpoint. The comparison of evaluation metrics (Tables 2 and 3) for the two implementation scenarios of this study, with oversampling during the training and without, highlights an increase in the robustness of the models. Indeed, every model showcases better all-around prediction performance when the random oversampling technique is used during its training (Table 3). More specifically, without oversampling the sensitivity of every model was higher than when employing oversampling (Table 2). This, basically, means that the prediction systems were better trained to predict adenoma cases. However, there is a significant increase in the specificity with oversampling and an increase in the overall accuracy of the models as well. Therefore, with oversampling, every model could not only definitely classify MG cases much more accurately but also showcase an overall enhancement in performance. The reason behind this is straightforward, since the oversampled training  
Figure 4: Summary plot illustrating the relative importance of each input feature in the prediction model as indicated by the SHAP values. subset includes more instances of the least prevalent class, it allowed the ML algorithm to be trained in a more balanced way. On the other hand, the comparison between the features identified as most influential by [PERSON]'s effect sizes and their corresponding SHAP values reveals a high degree of similarity (Figures 3 and 4, respectively). The results suggest that the number of deficiencies is the most impactful feature, followed by the Wisconsin index and the maximum diameter of the deficiency. These findings are consistent with the known risk factors for PHPT, as documented in the medical literature [58]. Overall, the [PERSON]'s effect size chart and the SHAP summary plot provide valuable insights into the factors influencing PHPT and are consistent with existing medical knowledge. The diagnosis of MGD preoperatively is quite difficult, though it is very substantial to reduce the risk of surgery failure. Even with the use of all available imaging/clinical data, it is quite hard to predict the presence of multiple parathyroid lesions in some cases. Previous studies established scoring systems based on imaging/clinical data for predicting MGD. [PERSON] et al. [17] built a scoring model based on the features of 4D-CT, in combination with biochemical information, achieving a specificity of 81-96% and a variable sensitivity of 39-64% for predicting MGD. More recently, [PERSON] et al. [18] developed a nomogram based on US findings and clinical factors to predict MGD in PHPT patients, with a specificity of 0.94 and a sensitivity of 0.50. The number of abnormal glands identified in imaging studies is the most important factor indicating MGD. In our study, the number of abnormal glands (feature \"number of defs\") proved to have the best prediction toward MGD. The Wisconsin index shows a predilection towards MGD, as is expected from the existing literature [15], since a low Wisconsin index is indicative of MGD. High values of the max diameter index (indicating small glandular size) surprisingly show a predilection toward SD, instead of MGD, as expected.