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Прогнозирование эффективности химиотерапии при лечении онкологических заболеваний.

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Summary
Demyanov V. F ., Demyanova V. V. , Kokorina A . V., Moiseenko V. M. On prognosing the
efficiency of chemotherapy in the treatment of oncological patients.
A methodology of prognosing the efficiency of chemotherapy in the treatment of oncological
patients is described. There exist two databases: one on patients who have had their cancer excited
and obtained chemotherapy, and another database on the patients who have also had their cancer
excited but no chemotherapy was applied to them. The following problem is studied: to find
a criterion allowing, for any patient, to give a prognosis for the time of his survival in the case
chemotherapy is applied to treat him and in the case it is not used.
JIHTepaTypa
1. Wolberg W . H. , Lee Y.-J., Mangasarian O. L. WPBCC: Wisconsin Prognostic Breast
Cancer Chemotherapy Database / / Computer Science Dept ., University of Wisconsin, Madison
(ftp://ftp.cs.wisc.edu/math-prog/ epo- dataset/machine-learn/cancer/WPBCC/, 1999) .
2. Lee Y.-J., Mangasarian O. L., Wolberg W. H. Survival-time classification of breast cancer
patients / / Computational Optimization and Applications. 2003. Vol. 25. P . 151-166.
3. Lee Y.-J. , Mangasarian O. L. SSVM : A smooth support vector machine for classification / /
Computational Optimization and Applications. 2001. Vol. 20 , N 1. P. 5- 22.
4. Advances in kernel methods. Support vector learning / Eds. B. Schoelkopf, C. J. C. Burges,
A. J. Smola. Cambridge, Mass.; London, England: The MIT Press, 1999. 392 p.
5. B ennett K. P., Mangasarian O. L. Robust linear programming discrimination of two linearly
inseparable sets / / Optimization Methods and Software. 1992. Vol. 1, N 1. P. 22-34.
6. Bagirov A. M., Rubinov A. M., Soukhoroukova N. V., Yerwood J. Unsupervised and super­
vised data classification via nonsmooth and global optimization / / Theory of Optimization. 2003.
Vol. 11, N 1. P. 1-93.
7. Kokorina A. V. Unsupervised and supervised data classification via nonsmooth and global
optimization / / Theory of Optimization. 2003. Vol. 11, N 1. P. 86- 89.
8. K okorina A. V. Ranking the parameters in classification databases / / Longevity, aging and
degradation models. Vol. 2 (MaTepHaJIbI Me)K,lU'Hap. KOH<pepeHlJ;liH LAD'2004). CI16.: 113,1l;-BO
C.-I1eTep6. rrOJIIiTeXH. YH-Ta, 2004. C. 191-193.
9. ,l(eMbJI:H,06a B. B. MeTo.n; rJIaBHOrO 3KcrrepTa B 3a.n;aQaX H.n;eHTH<pHKaU;HH / / Tpy.n;bI Me)K,lU'­
Hap. KOH<pepeHIl,HH «YcTOnQHBOCTb H rrpou;eccbI yrrpaBJIeHH5I» (C.-I1eTep6ypr, 29 HIOH5I - 1 HIOJI5I
2005 r.) / Pe.n;. ,2J;. A. OBC5IHHHKOB, JI. A. I1eTpOC5IH. CI16.: 113.n;-Bo C.-I1eTep6. YH-Ta, 2005. T. 2.
C. 815-822.
10. Demyanova V. V. The principal expert method in data mining / / Applied Comput. Math.
2005 . Vol. 4, N 1. P. 70-74.
CTaTMI nocTynHJIa B pe.n;aKu.mo 7 HIOHH 2006 r.
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