CHAIR OF

COMPUTATIONAL

INTELLIGENCE

The typical way to analyze clinical data is to use one performance metric and extract the most important features by performing dimensionality reduction mechanisms. In this paper, we identify several performance metrics describing data of patients with Parkinson’s disease and observe a large variability of their performance when we consider these metrics separately. None of the patients has the same performance in all parameters, some are better in one and worse in others. This feature is well-known in the context of multi-objective optimization. In this paper, we propose a clustering of data based on multi-objective analysis and perform a correlation-based feature selection with statistical testing to quantify and understand the variability in the clinical data.