Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping.

ABSTRACT: Explainable and interpretable machine learning has emerged as essential in leveraging artificial intelligence within high-stakes domains such as healthcare to ensure transparency and trustworthiness. Feature importance analysis plays a crucial role in improving model interpretability by pinpointing the most relevant input features, particularly in disease subtyping applications, aimed at stratifying patients based on a small set of signature genes and biomarkers. While clustering methods, including unsupervised random forests, have demonstrated good performance, approaches for evaluating feature contributions in an unsupervised regime are notably scarce. To address this gap, we introduce a novel methodology to enhance the interpretability of unsupervised random forests by elucidating featu

SUBMITTER: Sirocchi C 

PROVIDER: S-EPMC11829558 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature