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Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

Medicine and Health

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

M. Karabacak, P. Jagtiani, et al.

This innovative research by Mert Karabacak, Pemla Jagtiani, Alejandro Carrasquilla, Isabelle M. Germano, and Konstantinos Margetis harnesses machine learning to predict survival outcomes for glioma patients, transforming clinical decision-making with personalized analytics integrated into a user-friendly web application. The predictive models, utilizing LightGBM and Random Forest, demonstrate impressive accuracy, ensuring that neuro-oncology practices are more data-driven and tailored to individual patient needs.

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