A survey of prediction models for breast cancer survivability

Authors:
Amna Ali;Ali Tufail;Umer Khan;Minkoo Kim
Affiliations:
Ajou University, South Korea;Ajou University, South Korea;COMSATS Institute of Information Technology, Sahiwal, Pakistan;Ajou University, South Korea
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

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Abstract

Breast cancer prognosis poses a great challenge to the researchers. Recently, there have been breakthroughs in the field of bioinformatics and because of that a new realm of breast cancer prognosis has opened. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer prognosis. In this paper we present a survey of those models that are being used to enhance the breast cancer prognosis prediction. Firstly, we introduce these models and secondly we give an overview of the current research being carried out using these models. We specify different level of accuracies being claimed by different researchers. Lastly, we conclude that despite the ongoing research efforts towards achieving better capabilities for prediction system, we still need much more to build a more accurate and less invasive prognostic system that can benefit the mankind.