Machine learning algorithms for time to event data analysis

Music City Center 

Survival analysis is a subfield of statistics to analyze survival model with time to event data. In the literature, there are several statistical approaches have been widely developed to study the time to event data in survival analysis. In this paper, we consider traditional survival models such as Cox-proportional hazard model, accelerated failure time model and mixture cure model. The mixture cure model deal with population that consists with susceptible and unsusceptible individuals. Machine learning algorithms have been applied in the field of survival analysis to deal with more complex datasets and to predict the time to event outcomes. We consider random survival forest, survival support vector machine, and neural network machine learning approach of survival analysis. Furthermore, we apply real datasets to compare the performance of traditional survival analysis approaches and machine learning based survival methods.

Survival data, right censored, survival random forest, survival support vector machine 

Lifetime Data Science Section