Machine learning-based prediction of glioma grading

Eleven key features have been identified that facilitate molecular detection and personalized targeted therapy for glioma. Nine models were developed and optimized, and the RF model was observed to provide the best performance, potentially guiding future ML-related research in glioma. Additionally, the voting ensemble method, which integrates RF, XGBoost, and KNN, was shown to achieve superior performance, thereby enhancing both accuracy and robustness. Finally, all the models were successfully validated on the CGGA dataset, indicating strong generalizability.