Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction.

ABSTRACT: Background/Objectives: Gliomas are complex and heterogeneous brain tumors characterized by an unfavorable clinical course and a fatal prognosis, which can be improved by an early determination of tumor kind. Here, we developed explainable machine learning (ML) models for classifying three major glioma subtypes (astrocytoma, oligodendroglioma, and glioblastoma) and predicting survival rates based on RNA-seq data. Methods: We analyzed publicly available datasets and applied feature selection techniques to identify key biomarkers. Using various ML models, we performed classification and survival analysis to develop robust predictive models. The best-performing models were then interpreted using Shapley additive explanations (SHAP). Results: Thirteen key genes (TERT

SUBMITTER: Vershinina O 

PROVIDER: S-EPMC12384855 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature