Interpretable Machine Learning in Endocrinology: A Diagnostic Tool in Primary Aldosteronism

The use of urinary steroid metabolomics (USM) in combination with machine learning in endocrinology is briefly introduced. We demonstrate the usefulness of the approach for the detection and differential diagnosis of Primary Aldosteronism (PA), which has been addressed in a recent retrospective study. Here, we mainly present results for the application of the prototype based Generalized Matrix Relevance Learning Vector Quantization for the classification of steroid metabolomics profiles. The method allows for the successful diagnosis of PA and provides insights into the importance of the available markers. Moreover, it facilitates the non-invasive identification of a subtype of PA which is associated with adrenal adenoma harboring a particular mutation in the tumor tissue.