Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.

ABSTRACT:

Background

The Cox regression model is not sufficiently accurate to predict the survival prognosis of nasopharyngeal carcinoma (NPC) patients. It is impossible to calculate and rank the importance of impact factors due to the low predictive accuracy of the Cox regression model. So, we developed a system. Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients. This approach aimed to make up for the insufficiency of the Cox regression model. We also used the Cox regression model to validate the development of the nomogram and compared it with machin

SUBMITTER: Xiao Z 

PROVIDER: S-EPMC10774032 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature