## 5 Conclusions  
The main objective of this work was to overcome the restrictions a highly imbalanced dataset presents for classification and to create an all-around accurate classification ML system for PHPT instances. Indeed, a clear increase in specificity was observed, ranging from 11.11% to 37.04%, and the overall accuracy was enhanced as well. Additionally, much-needed insight into a black-box prediction system for PHPT classification was derived. The incorporation of explainability mechanisms such as SHAP analysis and Cohen's effect sizes provides transparency into the decision-making process of the model. This allows for a better understanding of the factors that contribute to its predictions and helps establish trust between the AI model and the human medical user. By highlighting the features that have the most significant impact on the prediction, the user can assess the reasoning behind the prediction and potentially identify any errors or outliers in the input data. This is particularly crucial in the medical field, where the accuracy and reliability of predictions can have a significant impact on patient outcomes. Therefore, incorporating explainability mechanisms can enhance the usefulness and trustworthiness of AI models in medical decision making. Another key takeaway of this study is that the AI classification system is consistent with contemporary research on PHPT, as evidenced by the alignment of the most common discerning factors for PHPT, with high SHAP values and Cohen's Effect sizes. This can enhance confidence in the system, particularly when it produces unexpected outputs. Moreover, the explainability outcomes provide legitimacy to the computer-aided decision-making system as they are consistent with years of research findings. Moving forward, our future plans include incorporating image data into our prediction models or incorporating prediction results from AI models that utilize image data as input. Moreover, we intend to conduct further research on the findings of Section 2, particularly on additional well-established PHTP risk factors.  
Conceptualization, N.P. and C.K.; design and conduct of the study, A.-D.S., M.T. and S.M.; data collection, M.T., S.V., P.Z. and C.K.; data processing A.-D.S. and M.T.; methodology, A.-D.S., S.M., E.P. and N.P.; validation, M.T., S.V., C.K. and N.P.; manuscript preparation, A.-D.S. and N.P.; manuscript review, M.T., S.M., P.Z., C.K. and E.P.; medical expertise, M.T., S.V., P.Z., N.P. and C.K.; visualization, A.-D.S.; supervision, N.P. and C.K.; project administration, E.P. and C.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** The retrospective nature of the study waives the requirement to obtain informed consent from the participants. All data-related processes were performed anonymously. All procedures in this study were in accordance with the Declaration of Helsinki.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company AIDEAS OU. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.