Performance Evaluation of Different Supervised Machine Learning Algorithms in Predicting Linear Accelerator Multileaf Collimator Positioning's Accuracy Problem

Abstract

Radiation Oncology is one of the businesses that employs Machine Learning to automate quality assurance tests so that errors and defects can be reduced, avoided, or eliminated as much as possible during tumor therapy using a Linear Accelerator with MultiLeaf Collimator (Linac MLC). The majority of Machine Learning applications have used supervised learning algorithms rather than unsupervised learning algorithms. However, in most cases, there is a clear bias in deciding which supervised machine learning algorithm to use. And prediction findings may be less accurate as a result of this bias. As a result, in this study, an evidence is presented for a novel application of Logistic Regression technique to predict Linac MLC positioning accuracy, which achieved 98.68 percent prediction accuracy with robust and consistent performance across several sets of Linac data. this evidence was obtained by comparing the performance of various supervised machine learning algorithms (i.e. Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Naive Bayes, and K-Nearest Neighbor) in the prediction of Linac MLC's positioning accuracy problem using leaves' positioning displacement datasets with labelled results as training and test datasets. For each method, two parameters were used to evaluate performance: prediction accuracy and the receiver operating characteristics curve. Based on that evaluation, the right selection sequence was proposed for supervised Machine Learning algorithms in order to achieve near-optimal prediction performance for Linac MLC's leaf positioning accuracy problem. As a result, the selection bias, as well as the negative side effects (i.e. ineffective preventive maintenance plan for Linac MLC to avoid and solve causes of inaccurate leaf displacement such as motor fatigue and stuck problems) could have occurred were successfully avoided.