A machine learning model for predicting oligoclonal band positivity using routine cerebrospinal fluid and serum biochemical markers.

ABSTRACT:

Objective

To develop and validate a machine learning model for predicting oligoclonal band (OCB) positivity using routine cerebrospinal fluid (CSF) and serum biochemical markers to improve the diagnostic accuracy and efficiency of assessing intrathecal immunoglobulin G (IgG) synthesis.

Methods

In this retrospective study (n = 1709), an ensemble model was developed using 8 refined CSF and serum parameters. Combining optimized CatBoost, XGBoost, and LightGBM classifiers, the model was trained and evaluated using a 2-phase workflow, including 5-fold cross-validation and validation on independent internal (n = 342) and external (n = 49) cohorts.

Results

The developed ensemble model achieved a receiver operating characteristic-area under the curve (ROC-AUC) of 0.902 on the

SUBMITTER: Gozgoz H 

PROVIDER: S-EPMC12782304 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature