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

## TL;DR

This study creates a machine learning model that predicts OCB positivity using routine lab data, improving diagnostic accuracy for intrathecal IgG synthesis.

## Contribution

A novel machine learning ensemble model that outperforms the conventional IgG index in predicting OCB positivity.

## Key findings

- The model achieved an ROC-AUC of 0.902 on the internal test set, outperforming the IgG index (ROC-AUC 0.795).
- It demonstrated 90% accuracy and 96% sensitivity on the external validation cohort.
- The model combines optimized CatBoost, XGBoost, and LightGBM classifiers for improved performance.

## Abstract

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.

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.

The developed ensemble model achieved a receiver operating characteristic–area under the curve (ROC-AUC) of 0.902 on the internal test set, significantly outperforming the conventional IgG index (ROC-AUC, 0.795). At its optimal threshold, the model demonstrated an accuracy of 0.830, with a sensitivity of 0.714 and a specificity of 0.916. On the external validation cohort, it achieved 90% accuracy and 96% sensitivity.

A novel machine learning ensemble model accurately predicts OCB positivity using routine laboratory data and demonstrates superior performance compared with the IgG index. This approach represents a significant step in applying artificial intelligence in laboratory medicine, with the potential to enhance diagnostic efficiency. Prospective, multicenter validation is essential for broader clinical implementation.