  Multidimensional data analysis has become increasingly important in many
fields, mainly due to current vast data availability and the increasing demand
to extract knowledge from it. In most applications, the role of the final user
is crucial to build proper machine learning models and to explain the patterns
found in data. In this paper, we present an open unified approach for
generating, evaluating, and applying regression models in high-dimensional data
sets within a user-guided process. The approach is based on exposing a broad
correlation panorama for attributes, by which the user can select relevant
attributes to build and evaluate prediction models for one or more contexts. We
name the approach UCReg (User-Centered Regression). We demonstrate
effectiveness and efficiency of UCReg through the application of our framework
to the analysis of Covid-19 and other synthetic and real health records data.
