Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications

Clinical datasets usually carry numerous features (biomarkers, characteristics, etc.) concerning the examined populations. This fact, although beneficial, challenges the statistical analysis via standard univariate approaches. In the two-sample setting, the majority of the clinical studies evaluate their assumptions relying on a variety of available univariate tests, such as the Student's t-test or Mann-Whitney Wilcoxon. We developed an easy-to-use-and-interpret non-parametric two-sample hypothesis testing framework (ts-AUC) particularly using machine learning and the AUC maximization criterion. We test and verify the effectiveness of ts-AUC in real data containing posturographic features of Parkinsonian patients (PS) with and without history of falling.