Abstract—Cox Proportional Hazard model and the associated
Partial Likelihood criterion have been the main tool of inference
for data of survival studies. There are many powerful techniques
in modern machine learning which could be applied on the
survival studies and increase the performance. This paper
consider the problem of comparing different techniques for
inference with large dimensional data of breast cancer. Besides
an overview of the different techniques, numerical experiments
are presented. Liu Yang managed to implement the algorithms
and the experiments. Kristiaan Pelckmans managed to complete
the theoretical part.
Index Terms—Machine learning, survival analysis, boost-ing, SVM.
Liu Yang is with the Department of Information Technology, Uppsala Universtiy, 75105 Uppsala, Sweden (e-mail: firstname.lastname@example.org). Kristiaan Pelckmans is with the Department of Information Technology, Uppsala Universtiy, 75105 Uppsala, Sweden.
Cite: Liu Yang and Kristiaan Pelckmans, "Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 483-490, 2014.