Predicting the Risk of Lumbar Prolapsed Disc: A Gene Signature-Based Machine Learning Analysis.

ABSTRACT:

Introduction

Lumbar prolapsed disc (LPD) is a leading cause of low back pain, contributing significantly to global disability and healthcare burden. This study aimed to develop machine learning models to predict the risk of LPD by analysing gene expression profiles for early detection.

Methods

Transcriptomic data from peripheral blood samples were obtained from the Gene Expression Omnibus (GEO) database, with dataset GSE150408 used for training and GSE124272 for testing. The training dataset included 17 patients with sciatica resulting from LPD, all of whom had magnetic resonance imaging confirmation of single-level LPD at either the L4/5 or L5/S1 levels. Data from 17 healthy volunteers were used as controls. Recursive feature elimination (RFE) was employed to identify the m

SUBMITTER: Wang F 

PROVIDER: S-EPMC12085505 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature