Ensemble Learning for Prediction of Toxicity in Prostate Cancer Radiotherapy: Comparison Between Stacking and Genetic Algorithm Weighted Voting

Prediction of urinary toxicity after prostate cancer radiotherapy (RT) is remarkably challenging. Not only it is a multifaceted phenomenon, encompassing different symptoms (retention, dysuria, haematuria, etc.), but also a multifactorial problem, as it depends on both patient-specific clinical factors, individual biological parameters, and dosimetric patterns. Thus, there are a plethora of potential predictors compared to the paucity of available symptom-specific toxicity data. On top of that, in elder patients, urinary complications are not necessarily treatment-related which introduces important noise to the urinary toxicity assessment. In recent years, a growing interest in machine learning (ML) appears within the radiotherapy community. The goal of ML algorithms is to learn from existing data to recognize patterns in the population and make informed decisions. The purpose of this study was to implement two advanced heterogeneous ensemble methods, namely Stacking and Genetic Algorithm-based Weighted Average Voting for improving urinary toxicity prediction in the case of prostate cancer radiotherapy. Our analysis demonstrated that both GA-based Voting (AUC =0.66) and Stacking (AUC =0.80) outperformed the standard Weighted Voting classifier (AUC =0.66). In conclusion, Genetic Algorithm-based Weighted Average Voting may improve prediction performance compared to individual classifiers or conventional voting ensembles but at high computational cost. Stacking, on the other hand, appears significantly more powerful for predicting urinary toxicity at less computational cost.