What question did this study set out to answer?

The aim is to develop machine learning models that utilize routine clinical data for diagnosing osteoporosis in rheumatoid arthritis patients.

February 28, 2026Open Access

Application of a machine learning model based on routine clinical parameters for the diagnosis of rheumatoid arthritis with concomitant osteoporosis: a retrospective study

Puntos clave

The aim is to develop machine learning models that utilize routine clinical data for diagnosing osteoporosis in rheumatoid arthritis patients.
Analyzed data from 396 hospitalized patients with rheumatoid arthritis.
Developed multiple machine learning classifiers including support vector machines and random forests.
Employed soft-voting ensemble techniques to combine classifier outputs.
Conducted SHAP analysis to identify contributions of specific clinical parameters.
The support vector machine model achieved an AUC of 86.5%.
Random forest demonstrated the highest accuracy at 81.5%.
Ensemble model showed balanced performance with AUC of 83.2% and accuracy of 81.1%.
Age, sex, and BMI were identified as major factors influencing the model predictions.

Resumen

Rheumatoid arthritis (RA) is commonly complicated by secondary osteoporosis (OP), affecting up to 80% of patients. Although dual-energy X-ray absorptiometry (DEXA) is the diagnostic gold standard, its limited accessibility highlights the need for alternative tools. In this retrospective cohort study of 396 hospitalized RA patients, we developed machine learning models using demographic and routine laboratory data to identify concomitant OP. Five classifiers were evaluated and combined via a soft-voting ensemble. The support vector machine achieved the highest area under the curve (AUC) (86.5%), while the random forest showed the highest accuracy (81.5%). The ensemble model demonstrated balanced performance (AUC 83.2%; accuracy 81.1%). SHapley Additive exPlanations (SHAP) analysis indicated age, sex, and body mass index (BMI) as major contributors, whereas albumin and inflammatory markers—including platelet-to-lymphocyte ratio (PLR), neutrophil percentage/albumin ratio (NPAR), white blood cell count (WBC), and neutrophil-to-lymphocyte ratio (NLR)—showed modest but heterogeneous influences on model predictions. These findings suggest that machine learning models incorporating routinely collected clinical data offer a practical and interpretable approach for preliminary OP risk assessment in RA. However, given the single-center design and limited sample size, the results should be considered exploratory, and larger external validation studies are warranted.

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Cite This Study

Wu et al. (Fri,) studied this question.

synapsesocial.com/papers/69a288170a974eb0d3c04061 https://doi.org/https://doi.org/10.7717/peerj.20852

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