Abstract
Diverse machine learning models have applied to cancer survivability prediction. But most of them tend to report only the performance of the model. However, in order to help medical specialists to establish a treatment plan by using machine learning models, it is more pragmatic to elucidate which variables (markers) have most significantly influenced to the resulting outcome of cancer. This motivated us to propose a hybrid approach of two machine learning models, semi-supervised learning co-training and decision trees. The former performs prediction for cancer survivability, and the latter post-processes the results mainly focusing on which variables are more highly ranked. The proposed method was tested on the breast cancer survivability problem based on the surveillance, epidemiology, and end results database for breast cancer (SEER).
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Nam, Y., Shin, H. (2013). A Hybrid Cancer Prognosis System Based on Semi-Supervised Learning and Decision Trees. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_79
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DOI: https://doi.org/10.1007/978-3-642-42042-9_79
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