Comparison of Machine Learning Methods for Predicting 3-Year Survival in Elderly Esophageal squamous cancer Patients Based on Oxidative Stress

Background There is currently a lack of machine learning model studies exploring the relationship between oxidative stress score (OSS) and the prognosis of elderly Esophageal squamous cancer(ESCC) patients. Methods This study included elderly ESCC patients who underwent curative resection surgery from January 2013 to December 2020. Machine learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed to construct a predictive model for 3-year overall survival (OS) for elder ESCC base on OSS. Results Patients were divided into derivation cohort and validation cohort, and consisted of 340 and 145 patients, respectively. 8 important features which were the most important factors influencing 3-year OS (pathological N stage, pathological T stage, tumor histological type, vascular invasion, CEA, OSS, CA 19-9, and the amount of bleeding) were included in training the RF, DT and SVM. In the derivation cohort, the RF model exhibited the highest predictive performance with an AUC of 0.975(0.962-0.987), while the DT model is 0.784(0.739-0.830) and the SVM is 0.879(0.843-0.916). In the external validation cohort showed the similar trend . Conclusion The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly ESCC patients after curative surgery.