Development and Validation of a Non-Invasive Prediction Model for Glioma-Associated Epilepsy: A Comparative Analysis of Nomogram and Decision Tree

We have introduced a machine learning prediction model for GAE detection in glioma patients based on multiomics data. This model can improve the prognosis of GAE by providing early warnings and actionable feedback and prevent or reduce pathological damage and neurobiochemical changes by implementing early interventions.