INTRODUCTION
REGRESSION
Over the last two decades, the complexity of external beam radiation therapy has increased significantly. Volumetric‐modulated arc therapy (VMAT) often requires more beam intensity modulation, which typically results in a higher degree of complexity and larger dosimetric uncertainty. Consequently, pretreatment verification before treatments is highly recommended to ensure safe delivery.For comparison between the calculated and measured dose distributions, the gamma index is commonly used, which combines both percentage dose difference and distance‐to‐agreement. According to the American Association of Physicists in Medicine (AAPM) Task Group 218 (TG‐218), 3%/2 mm with a 95% passing rate is recommended.Many studies using different treatment planning systems (TPS) and linacs have shown that the degree of plan complexity may affect the accuracy of dose calculations and treatment delivery,Recently, machine‐learning (ML) and deep learning methods have been widely used to predict patient‐specific QA results. Granville et al. trained a support vector classifier with a linear kernel to predict VMAT patient QA results.According to previous studies, ML has been shown to be useful for predicting passing rates. Based on our literature review, support vector regression (SVR) with linear kernel was not used before for predicting GPR values. Consequently, in this study, we used SVR with the linear kernel to predict the values of GPR and compare it with random forest regression (RFR), whereby its accuracy for GPR prediction has been shown in previous literature.