A precise ensemble machine learning framework for improved glioma grading and classification with explainable AI

Gliomas, a common type of brain tumor, present a complex diagnostic challenge owing to their heterogeneity and the need for accurate grading to guide treatment decisions. This research explores the application of ensemble Machine Learning (ML) techniques to enhance the accuracy of glioma grading using the “Glioma Grading Clinical and Mutation Features” dataset. We introduce a rigorous methodology that includes the derivation of a novel feature, “ Number_of_mutations ”, to better capture the aggregate impact of genetic alterations. Our proposed framework involves comprehensive data preprocessing to prevent data leakage, extensive base model training with 5-fold cross-validation, and the implementation of stacking and voting ensemble architectures. The results indicate that a stacking model utilizing Gradient Boosting (GB) and Logistic Regression (LR) as base learners, with a meta-learner aggregation, achieves a high testing accuracy of 94.05%, precision of 88.46%, recall of 97.18%, F1-score of 93.24%, Matthews Correlation Coefficient (MCC) of 0.88, and an Area Under the Curve (AUC) score of 0.96. The model also demonstrates excellent calibration with a Mean Square Error (MSE) of 0.06 and Root Mean Square Error (RMSE) of 0.24, outperforming prior studies on the same dataset. Feature contributions are further illuminated through Explainable AI (XAI) techniques, separating global feature importance via SHAP (SHapley Additive exPlanations) and local interpretability via LIME (Local Interpretable Model-agnostic Explanations). These highlight IDH1 and IDH2 as significant positive indicators and TP53 and PTEN as negative indicators. This study provides a robust, reproducible, and interpretable framework for glioma grading, specifically targeting the improvement of diagnostic accuracy and supporting informed clinical decision-making.