Abstract
Ovarian cancer is diagnosed in nearly a quarter of a million women globally each year. It has the highest mortality rate of all cancers for women. The prognosis for ovarian cancer patients is poor, particularly about 20% of ovarian cancers are found at an early stage. CA-125 test is used as a tumor marker. A high level of CA-125 could be a sign of ovarian cancer or other conditions. We use a machine learning technique to explore better knowledge and most important factors useful for detecting early-stage ovarian cancer by evaluating the significance of data between the amino acids and the ovarian cancer. Therefore, we propose a Fuzzy Rough with Support Vector Machine (SVM) classification model to mine suitable rules. In pre-processing stage, we use Fuzzy Rough set theory for feature selection. In post-processing stage, we use SVM to merit of dealing with real and complex data, performing quick learning and having good classification performance.
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Badria, F.A., Shoaip, N., Elmogy, M., Riad, A.M., Zaghloul, H. (2014). A Framework for Ovarian Cancer Diagnosis Based on Amino Acids Using Fuzzy-Rough Sets with SVM. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_37
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DOI: https://doi.org/10.1007/978-3-319-13461-1_37
Publisher Name: Springer, Cham
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