HIGHLIGHTS
SUMMARY
One of the many opportunities available to study this increase, as well as the classical clinical approaches, is to apply machine_learning (ML) as a basis on which to predict cancer survival. Classical survival analysis is a well-established methodology for estimating a patient`s probability of survival. The authors aim to evaluate survival of GC patients using the classical statistical method, Cox Proportional Hazards (CPH) and compare it with new generation algorithms such as Random Survival Forest (RSF), and Survival Support Vector Machine (SSVM), together with a new approach to quantify the power of . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.