Identification of individuals at high risk of developing rheumatoid arthritis: a balanced random forest model in a cohort of 1544 first-degree relatives.

ABSTRACT:

Objectives

To identify in a genetically susceptible population individuals at higher risk of developing rheumatoid arthritis (RA) using a classification approach combining known epidemiological risk factors, serological biomarkers, genetics, clinical signs and symptoms.

Methods

We used data from the prospective SCREEN-RA (Evaluation of a SCREENing strategy for Rheumatoid Arthritis) cohort of 1540 first-degree relatives of RA patients (RA-FDRs). The primary outcome was the development of RA. Additionally, we used seropositive inflammatory arthritis (IA) as a secondary outcome for exploratory analyses. Balanced random forest (BRF) models were fit and evaluated through fivefold cross-validation to avoid overfitting. We chose a classification threshold that targeted high sensiti

SUBMITTER: Aymon R 

PROVIDER: S-EPMC12666099 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature