Glioma Classification Using Harris Hawks-Driven Optimized Gradient Boosting Classifier Along with SHAP-Based Interpretability

Gliomas are considered one of the most lethal and aggressive types of brain cancer, responsible for countless deaths worldwide. This study seeks to improve glioma classification using cutting-edge machine learning (ML) techniques to differentiate between glioma subtypes based on clinical and genomic data. The goal is to identify important biomarkers and features influencing glioma classification, with an emphasis on improving feature selection and model interpretability. For glioma classification, the Gradient Boosting Classifier (GBC) was employed. The Harris Hawks Optimization (HHO) algorithm was used for feature selection and hyperparameter fine-tuning to enhance the model’s performance. Additionally, SHapley Additive exPlanations (SHAP) were applied to improve model interpretability and to better understand feature contributions.The Gradient Boosting (GB) method yielded the best performance among the selected models, achieving an accuracy of 88.40%, precision of 87.3%, recall of 88.48%, and an F1 score of 88.29%, with feature selection and hyperparameter tuning using the Harris Hawks Optimization. These results highlight the significance of hyperparameter tuning and feature selection in enhancing classification performance. Key features such as IDH1, Age at Diagnosis, and EGFR were identified as the most influential in distinguishing glioma subtypes. SHAP analysis further confirmed the importance of these features in the model.This study shows that the Gradient Boosting Classifier (GBC), optimized with Harris Hawks Optimization (HHO), significantly improves glioma classification, achieving a high F1 score. Key features like IDH1, Age at Diagnosis, and EGFR were identified, showcasing its potential for enhanced glioma diagnosis.