Preliminary study on the design of a predictive structural model for daily quality assurance in proton beams

Abstract

The current quality assurance (QA) processes are conducted on an hourly, daily, and monthly basis, and these labor-intensive activities increase the demand for predictive models. The purpose of this study is to design a predictive structure for QA. We utilized the nonlinear autoregressive (NAR) neural network to predict the time series. To evaluate the capability of the predictive model, this study involved comparing the actual QA values to the values predicted by the NAR model for the sampled daily output. In predicting the daily output, our study determined that the optimal configuration for the NAR model included 1 input, 1 hidden layer with 20 neurons, and 1 output layer with a single output neuron. Considering the mean absolute percentage error (MAPE) as the evaluation criterion, the NAR model is 1.385. This indicates that the NAR model has clinical utility as a prediction model. The NAR model predicted within ± 0.2% compared to the actual output. Therefore, this study recommends the implementation of a predictive structural model as the next step in enhancing the QA system.