Predicting Survival Of Advanced Laryngeal Squamous Cell Carcinoma: Comparison Of Machine Learning Models And Cox Regression Models

This article discusses the development of a machine learning model to predict disease-free survival (DFS) in advanced laryngeal squamous cell carcinoma (LSCC) patients. The model was based on data from 671 patients and employed a random forest (RF) ensemble type classification method. The model showed good sensitivity and specificity in predicting DFS in the training cohort, but suboptimal performance in the validation cohort. The Cox regression model and random survival forest both showed good predictive ability.