Glioblastoma (GBM) is the most common primary brain tumor in adults with 15 months median overall survival. The purpose of this study was to identify overall survival of GBM patients based on clinical and Visually AcceSAble Rembrandt Images (VASARI) features using machine learning. According to our results, a support vector machine (SVM) model worked better for categorical data classification. With the help of adaptive synthetic (ADASYN) oversampling, a fine Gaussian SVM model identified short overall survival at 12 and 24 months thresholds with 99.78% and 88.80% accuracies, respectively.
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