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

## TL;DR

This study develops an interpretable machine learning model to assess osteoporosis risk in rheumatoid arthritis patients, identifying key risk factors and improving personalized prevention strategies.

## Contribution

The novel CNN-SVM algorithm integrated with SHAP and Sankey diagrams provides interpretable osteoporosis risk assessment in rheumatoid arthritis patients.

## Key findings

- The CNN-SVM model achieved AUC values of 0.83, 0.93, and 0.74 for different classification tasks.
- Key predictors like Vitamin D supplements, synovitis in both knees, and gender were crucial for distinguishing normal from osteopenia.
- Alendronate Sodium, weight, and age were consistently influential in differentiating osteoporosis.

## Abstract

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.

We recruited 314 RA patients from the Department of Rheumatology and Immunology. Participants were categorized into osteoporosis, osteopenia, and normal groups based on lumbar spine or hip bone mineral density (BMD) T-scores. We constructed ML model to assess osteoporosis using a novel classification algorithm, CNN-SVM, and employed SHapley Additive exPlanations (SHAP) and Sankey diagram to investigate significant risk factors, rank risk factor contributions, and provide individualized feature contribution explanations.

A total of 16 candidate variables were included, and three classification models were constructed to predict osteoporosis versus osteopenia, osteoporosis versus normal, and osteopenia versus normal. The AUC values for the models were 0.83, 0.93, and 0.74, respectively. Feature importance analysis using SHAP identified several key predictors. Factors such as Vitamin D supplements, Synovitis in Both Knees, and gender were crucial for distinguishing normal from osteopenia. For differentiating osteoporosis, Alendronate Sodium, weight, and age consistently ranked as highly influential features across different comparisons. Feature importance analysis was performed, ranking risk factors and providing individualized explanations of feature contributions.

The developed interpretable ML model shows promise for screening osteoporosis risk in patients with RA. Its ability to identify individual risk factors highlights its potential to facilitate personalized prevention and management strategies, pending further validation.

The online version contains supplementary material available at 10.1186/s12891-026-09526-1.