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A Framework for Ovarian Cancer Diagnosis Based on Amino Acids Using Fuzzy-Rough Sets with SVM

  • Conference paper
Advanced Machine Learning Technologies and Applications (AMLTA 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 488))

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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Author information

Authors and Affiliations

  1. Pharmacognosy Dept., Faculty of Pharmacy, Mansoura University, Egypt

    Farid A. Badria

  2. Information Systems Dept., Faculty of Computers and Information, Mansoura University, Egypt

    Nora Shoaip & A. M. Riad

  3. Information Technology Dept., Faculty of Computers and Information, Mansoura University, Egypt

    Mohammed Elmogy

  4. Dept. of Clinical Pathology, Faculty of Medicine, Mansoura University, Egypt

    Hosam Zaghloul

Authors
  1. Farid A. Badria
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  2. Nora Shoaip
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  3. Mohammed Elmogy
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  4. A. M. Riad
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  5. Hosam Zaghloul
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Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Benha University, Benha, Egypt

    Ahmad Taher Azar

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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

  • Print ISBN: 978-3-319-13460-4

  • Online ISBN: 978-3-319-13461-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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