The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach

"This intervention uses a machine learning model to predict the risk of recurrent lumbar disc herniation (rLDH) in patients who have had percutaneous endoscopic interlaminar discectomy (PEID) at the L5-S1 level. The model combines clinical data (e.g., BMI, disease duration, diabetes) and imaging metrics (e.g., posterior disc height index, spinal canal stenosis) to create a personalized risk score, unlike traditional methods that rely on clinical judgment or imaging alone.~Key Features:~Data-Driven Approach: Developed using data from 309 patients for real-world relevance.~Advanced Variable Selection: Identifies eight key predictors using LASSO regression.~Multiple Machine Learning Techniques: Uses algorithms like support vector machine, random forest, and extreme gradient boosting.~Optimized for Clinical Decision-Making: Assists surgeons in personalizing treatment plans to reduce recurrence risk."