With the ability to analyze large amounts of data, such systems have the potential to improve accuracy in the detection of diseases, allowing for earlier intervention and treatment. Additionally, AI can assist in the development of new drugs, by predicting the behavior of molecules and identifying potential targets for therapy. With the use of AI, physicians can spend more time with patients, leading to better patient experiences and outcomes. While AI in healthcare is still in its early stages, it has the potential to transform the industry and improve patient care, creating a more efficient and effective healthcare system. The subject of PHPT prediction using ML models has been tackled by the research community, mainly with the use of image data [10]. One such study [11] employed a deep learning algorithm to classify PHPT based on clinical, laboratory, and imaging data. The study found that a modified DenseNet architecture was able to accurately classify PHPT with a sensitivity of 90.6% and a specificity of 94.5%. In ref. [12], a boosted tree classifier, RandomTree, was used to predict PHPT based on clinical and laboratory data, with a maximum accuracy of 94.1%. Moreover, researchers in [13] used ML models to predict the risk of postoperative hypocalcemia in patients undergoing parathyroidectomy for PHPT. This study found that the prediction model was able to accurately differentiate an abnormal versus a normal parathyroid gland with a precision-recall characteristic curve of 0.93. Finally, another study [14] used deep learning (DL) methodologies, in order to develop a prediction model for persistent PHPT after parathyroidectomy. The study found that the prediction model had a maximum achievable sensitivity of 96.3% and a specificity of 97%, suggesting that it could be a useful tool for guiding postoperative management. Overall, these studies demonstrate the potential for ML to improve the diagnosis and management of PHPT. However, further research is needed to validate these findings and develop ML-based tools that can be effectively integrated into clinical practice. It Immediately becomes clear that in the scientific pool of relevant work, the vast majority of studies use datasets consisting of, or accompanied by, image data. In contrast, the current work utilizes a small feature set, that focuses mainly on three indexes: the maximum diameter of the deficiency index, the number of deficiencies index, and the Wisconsin index [15]. These three essential indices play pivotal roles in the evaluation and characterization of non-small cell lung cancer (NSCLC) cases. The maximum diameter index serves as a crucial metric, representing the largest dimension of a given lesion or tumor. It is instrumental in quantifying the size and extent of pathological findings, contributing to a comprehensive assessment of the lesions under consideration. The number of deficiencies index provides a quantitative measure of the identified abnormalities, offering insights into the count and distribution of deficiencies within the studied cohort. This index aids in understanding the overall burden and diversity of pathological manifestations, enriching the dataset analysis. The reported deficiencies are referring to the number of abnormal disorders of parathyroid glands depicted in ref. [16]. This means that when the number of deficiencies is greater than one they are defined as multigland diseases [17,18]. Additionally, the Wisconsin index, hailing from a well-established lineage of indices, encapsulates a comprehensive evaluation of NSCLC cases. Derived from a combination of features and measurements, this index reflects a different perspective on the complexities of the disease, thus contributing valuable dimensions to the overall dataset analysis. Together, these indices collectively enhance the study's statistical analyses and contribute to a more subtle understanding of NSCLC characteristics. We believe that by utilizing a small feature set, a highly adaptive but accurate methodology for PHPT classification can be created.