Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer Le Minh Thao Doan, Claudio Angione, and Annalisa Occhipinti Abstract Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost- effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early- detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. Le Minh Thao Doan School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK, e-mail: l.doan@tees.ac.uk Claudio Angione School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; Centre for Digital Innovation, Teesside University, Middlesbrough, UK; Healthcare Innovation Centre, Teesside University, Middlesbrough, UK; National Horizons Centre, Teesside University, Darlington, UK; e-mail: c.angione@tees.ac.uk Annalisa Occhipinti School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; Centre for Digital Innovation, Teesside University, Middlesbrough, UK; National Horizons Centre, Teesside University, Darlington, UK; e-mail: a.occhipinti@tees.ac.uk 1