Prediction of Glioma Patient Survival Using MRI-Derived Features and Automated Machine Learning (AutoML) Methods

Motivation: Predicting survival days for glioma patients is crucial for clinical decision-making and optimizing treatment strategies. Goal(s): This study aims to improve survival prediction for glioma patients by developing optimized machine learning models using a feature set derived from MRI data. Approach: We preprocessed the features extracted from BraTS2020 dataset, and refined to 170 using correlation analysis. We implemented Hyperopt-sklearn for AutoML-driven model selection and hyperparameter tuning to predict survival days. Results: The kNN model achieved F1 score of greater than 0.8 and a SGDR model performs best in the 301-400 days range, demonstrating the effectiveness and efficiency of AutoML over traditional methods. Impact: Our results show that AutoML methods like Hyperopt-sklearn can simplify and accelerate survival prediction for glioma patients, even for researchers with minimal programming skills, thereby broadening access to advanced data-driven tools in clinical research.