Abstract
The use of machine learning in disease prediction and prognosis is part of a growing trend of personalized and predictive medicine. Cancer studies are domain of active machine learning implementation in particular in sense of accuracy of cancer prognosis and prediction. The accuracy of survival time prediction in breast cancer is the main object of the study. Two major features for survival time prediction, based on clinical data are used: the created in the study tumor integrated clinical feature and Nottingham prognostic index. The applied machine learning methods aside with data normalisation and classification provide promising results for accuracy of survival time prediction. Results showed prepotency of the support vector regression modles - linear and decision tree regression models, for more accurate prediction of the survival time in breast cancer. Cross-validation, based on four parameters for error evaluation, confirms the results of the model performance concerning the accuracy of survival time prediction in breast cancer.
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Acknowledgements
The presented work has been funded by the Bulgarian NSF within the “GloBIG: A Model of Integration of Cloud Framework for Hybrid Massive Parallelism and its Application for Analysis and Automated Semantic Enhancement of Big Heterogeneous Data Collections” project, Contract DN02/9 of 17.12.2016, and by the Sofia University SRF within the “Models for semantic integration of biomedical data” project, Contract 80-10-207/26.04.2018.
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Mihaylov, I., Nisheva, M., Vassilev, D. (2018). Machine Learning Techniques for Survival Time Prediction in Breast Cancer. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_17
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