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

TL;DR

This paper introduces UCReg, a visual analytics framework that enables users to build and evaluate logistic regression models on high-dimensional health data, enhancing interpretability and decision-making.

Contribution

The paper presents a novel user-guided approach for constructing and assessing regression models in high-dimensional data, emphasizing visual correlation analysis.

Findings

Effective analysis of Covid-19 health records demonstrated the approach's utility.

The framework efficiently identified relevant attributes for prediction.

Application to synthetic data validated the method's robustness.

Abstract

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.