An Ensemble Learning Torching for Brain Tumor Patient Survival Prediction

This paper describes the ensemble learning framework that will help to improve survival prediction of brain tumor patients on the basis of multimodal clinical and imaging data. The suggested approach combines several machine-learning models, such as gradient boosting, random forests, and deep neural networks, into a single stacking framework that reduces the bias of each distinct model and provides a variety of feature representations. The process of preprocessing is used to enhance resilience and minimize overfitting by the use of feature selection, normalization, and data augmentation. The performance of the ensemble has been assessed on publicly available datasets of brain tumors, and it has been found to be more accurate and stable in its predictions than individual models. The data show that the combination of heterogeneous learners is the most effective way to enhance survival estimation and, as a consequence, more accurate prognostic evaluation and help the clinicians plan the personalized treatment. This framework underscores the promise of ensemble-based AI using solutions in the development of precision medicine in the management of brain tumors.