Application of Machine Learning Techniques for Prediction of Radiation Pneumonitis in Lung Cancer Patients

Abstract

Lung cancer patients who receive radiotherapy as part of their treatment are at risk radiation-induced lung injury known as radiation pneumonitis (RP). RP is a potentially fatal side effect to treatment. Hence, new methods are needed to guide physicians to prescribe targeted therapy dosage to patients at high risk of RP. Several predictive models based on traditional statistical methods and machine learning techniques have been reported, however, no guidance to variation in performance has not been provided to date. Therefore, in this study, we compare several widely used classification algorithms in the machine learning field are used to distinguish between different risk groups of RP. The performance of these classification algorithms is evaluated in conjunction with several feature selection strategy and the impact of the feature selection on performance is further evaluated.