The Prediction of Recurrence of Lumbar Disc Herniation at L5-S1 through Machine Learning Based on Endoscopic Discectomy <em>via</em> the Interlaminar Approach

This study aimed to develop machine learning (ML) models to predict the L5-S1 level recurrent lumbar disc herniation (rLDH) after percutaneous endoscopic interlaminar discectomy (PEID), a minimally invasive treatment for L5-S1 lumbar disc herniation. Data from 309 patients who underwent single-level L5-S1 PEID between January 2020 and June 2024, with at least 6 months of follow-up, were analyzed. Clinical records, preoperative imaging, and visual analog scale (VAS) scores were used. LASSO regression identified key predictors, and six ML models were built: support vector machine (SVM), decision tree (DT), adaptive boosting (ADA), light gradient boosting machine (LGBM), random forest (RF), and extreme gradient boosting (XGB). Among the patients, 10.7% experienced rLDH, defined as ≥60% VAS reduction followed by symptom recurrence and imaging confirmation. Key predictors included Body Mass Index (BMI), posterior disc height index (PDHI), spinal canal stenosis, disease duration, numbness or weakness, Modic changes, herniation type, and diabetes. The RF and XGB models performed best. Higher BMI, Higher PDHI, spinal canal stenosis, disease duration over six months, Modic changes, non-contained herniation, and diabetes increased rLDH risk. Variable importance was ranked for both models. Predicting rLDH preoperatively can enhance decision-making and reduce recurrence risk after PEID, with ML models improving accuracy and identifying critical risk factors.