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Breast Cancer Diagnosis Using Relational Discriminant Analysis of Malignancy-Associated Changes
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Author(s): Dmitry Klyushin (Kyiv National Taras Shevchenko University, Ukraine), Natalia Boroday (National Academy of Sciences of Ukraine, Ukraine), Kateryna Golubeva (Kyiv National Taras Shevchenko University, Ukraine), Maryna Prysiazhna (Kyiv National Taras Shevchenko University, Ukraine)and Maksym Shlykov (Kyiv National Taras Shevchenko University, Ukraine)
Copyright: 2017
Pages: 21
Source title:
Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch002
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Abstract
The chapter is devoted to description of a novel method of breast cancer diagnostics based on the analysis of the distribution of the DNA concentration in interphase nuclei of epitheliocytes of buccal epithelium with the aid of novel algorithms of statistical machine learning, namely: novel proximity measure between multivariate samples, novel algorithm of construction of tolerance ellipsoids, novel statistical depth and novel method of multivariate ordering. In contrast to common methods of diagnostics used in oncology, this method is a non-invasive and offers a high rate of accuracy and sensitivity.
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