When modelling survival data one usually starts with a Kaplan-Meier curve and uses then Cox regression for machine learning. I am wondering if I could use also more sophisticated methods as models such as neural networks or support vector regressors etc to model the survival data?
Let's say I am interested in predicting survival for 1 year; can I simply remove all data censored from people in the first year and use that as the dataset? This includes people that did not die at all, died after a year and were censored after a year as 'positive' examples and treat all people which died within a year as a negative example. Is this an ok method? I am aware I do throw out valuable information on people censored in the first year but can I make this assuption to make NNs and SVRs work?