Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients.

ABSTRACT:

Background

Most patients with advanced non-small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan. We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients. This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients.

Methods

Multiple machine-learning models were used in a retrospective cohort of 6586 patients. First, we modeled and validated a nomogram to predict the overall survival of NSCLC patients. Subsequently, five machine-learning models (logistic regression, random forest, XGBoost, decision tree, and l

SUBMITTER: Lei H 

PROVIDER: S-EPMC10686174 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature