Machine learning models predicts risk of proliferative lupus nephritis.

ABSTRACT:

Objective

This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe.

Methods

This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector mach

SUBMITTER: Yang P 

PROVIDER: S-EPMC11196753 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature