Precision Glioma Grading Using Voting-Based Feature Selection and Ensemble Machine Learning with Clinical and Molecular Data

Approximately 80,000 individuals in the United States are diagnosed annually with primary brain tumors, with gliomas accounting for nearly 25% (20,000 cases annually). Gliomas range from low-grade tumors median-survival sixteen years to high-grade malignancies glioblastoma multiforme (GBM)–median-survival eight months. In 2021, The World Health Organization released a revised classification of gliomas  based on molecular features. Molecular information has greatly enhanced precision diagnosis and facilitated treatment decisions. However, as molecular profiling techniques advanced, molecular mutation landscape data for gliomas continued to expand, necessitating testing across the growing number of mutations that numbers at least twenty by now. Such expensive molecular testing imposes a massive burden on cost and  resources.

Using publicly available TCGA glioma data, we implemented a voting-based feature selection framework integrating seven complementary feature selection methods. The selected features were evaluated using an ensemble of six machine-learning(ML) classifiers, producing 57 baseline ensemble configurations, which were expanded to 171 through the addition of hard voting, soft voting, and stacking strategies. The voting based and ensemble method was compared with standard Lasso-method for feature efficiency and diagnostic accuracy.

The voting-based framework reduced the optimal feature set to eight  molecular and clinical variables from the original set of 23 features, compared to fourteen features selected by Lasso, while maintaining similar predictive performances. Stacking ensembles—particularly SVM+SGD and AdaBoost+SVM+SGD—achieved the highest mean accuracy (0.881), and stacking ensembles using Lasso selected features reached a comparable peak accuracy (0.882) for glioma classification.

These findings demonstrate that voting-based feature selection and ensemble method significantly outperforms Lasso in feature efficiency while maintaining the same high predictive accuracy. By using a streamlined molecular feature set, this approach achieves cost-effective glioma grading with high diagnostic accuracy, while improving equity in healthcare access.

Dr Joshi is a senior internal medicine resident at Florida Atlantic University, College of Medicine. She will be pursuing a Nephrology Fellowship at University of Florida, Gainesville, FL from Jun, 2026. She is involved in teaching medical students and interns. She is also involved in the Quality Improvement Project at FAU college of medicine internal medicine clinic. She also served as a mentor for international medical graduates.