Elucidating osteoporosis response signatures in rheumatoid arthritis using explainable machine learning ensembles.

ABSTRACT:

Background and objectives

Osteoporosis (OP) presents a significant health issue in rheumatoid arthritis (RA) patients, yet existing machine learning (ML) studies on OP prediction in this population are limited by low accuracy, a narrow range of considered risk factors, and a lack of interpretability. This study aims to develop an interpretable machine learning model using the CNN-SVM algorithm, integrated with interpretability techniques, for individualized osteoporosis risk assessment in RA patients. The model specifically focuses on the osteopenia stage, which has been overlooked in previous research, to better capture the different risk factors involved in the progression of osteoporosis in RA patients.

Methods

We recruited 314 RA patients from the Department of Rheumatol

SUBMITTER: Lin K 

PROVIDER: S-EPMC12905921 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature