Subgroup Analysis via Model-based Rule Forest

TL;DR

This paper introduces mobDRF, an interpretable rule-based learning algorithm that extracts transparent models from data, improving interpretability without sacrificing accuracy, especially useful in healthcare for subgroup analysis.

Contribution

The paper presents mobDRF, a novel model-based rule forest method that enhances interpretability of machine learning models through multi-level logic rules, applied to healthcare data.

Findings

Successfully identified key risk factors for cognitive decline.

Enhanced interpretability of models without loss of accuracy.

Effective subgroup analysis in elderly population.

Abstract

Machine learning models are often criticized for their black-box nature, raising concerns about their applicability in critical decision-making scenarios. Consequently, there is a growing demand for interpretable models in such contexts. In this study, we introduce Model-based Deep Rule Forests (mobDRF), an interpretable representation learning algorithm designed to extract transparent models from data. By leveraging IF-THEN rules with multi-level logic expressions, mobDRF enhances the interpretability of existing models without compromising accuracy. We apply mobDRF to identify key risk factors for cognitive decline in an elderly population, demonstrating its effectiveness in subgroup analysis and local model optimization. Our method offers a promising solution for developing trustworthy and interpretable machine learning models, particularly valuable in fields like healthcare, where understanding differential effects across patient subgroups can lead to more personalized and effective treatments.