Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma.

ABSTRACT: The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The stud

SUBMITTER: Zhang K 

PROVIDER: S-EPMC10439907 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature