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Multidimensional data analysis has become increasingly important in manyfields, mainly due to current vast data availability and the increasing demandto extract knowledge from it. In most applications, the role of the final useris crucial to build proper machine learning models and to explain the patternsfound in data. In this paper, we present an open unified approach forgenerating, evaluating, and applying regression models in high-dimensional datasets within a user-guided process. The approach is based on exposing a broadcorrelation panorama for attributes, by which the user can select relevantattributes to build and evaluate prediction models for one or more contexts. Wename the approach UCReg (User-Centered Regression). We demonstrateeffectiveness and efficiency of UCReg through the application of our frameworkto the analysis of Covid-19 and other synthetic and real health records data.

A Visual Analytics Approach to Building Logistic Regression Models and its Application to Health Records