Patient Survival Prediction Using Data Mining Technique

Authors(1) :-Dr. T. Shanmuga Vadivu

With more and more biological information generated, the most pressing task of bioinformatics has become to analyze and interpret various types of data, including nucleotide and amino acid sequences, protein structures, gene expression profiling and so on. In this research, to apply the data mining techniques such as feature generation, feature selection, and feature integration with learning algorithms to tackle the problems of disease phenotype classification and patient survival prediction from gene expression profiles, and the problems of functional site prediction from DNA sequences. When dealing with problems arising from gene expression profiles, the researcher propose a new feature selection process for identifying genes associated with disease phenotype classification or patient survival prediction.

Authors and Affiliations

Dr. T. Shanmuga Vadivu
Assistant Professor, Department of Computer Science, Arulmigu Palaniandavar Arts College For Women, Palani, Tamil Nadu, India

Micro Array, Patient Survival Prediction, Support Vector Machine.

  1. H. Witten and E. Frank, 2000. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann, San Mateo, CA.
  2. C.J. Thornton, 1992. Techniques in Computational Learning. Chapman and Hall, London.
  3. T.M. Mitchell,1997. Machine Learning. McGrawHill, USA.
  4. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P.Mesirov, H.            Coller,M. L. Loh, J. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S Lander,1999.   Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531-537.
  5. A. Alizadeh, M.B. Eisen, R.E. Davis, C.Ma, I.S. Lossos, A. Rosenwald, J.C.    Boldrick,H. Sabet, T. Tran, X. Yu, J.I. Powell, L. Yang, G.E. Marti, T. Moore, J.Jr. Hudson, L. Lu,D.B. Lewis, R. Tibshirani, G. Sherlock, W.C. Chan, T.C. Greiner, D.D. Weisenburger,J.O. Armitage, R. Warnke, and L.M. Staudt, 2000. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403:503-511.
  6. M.A. Hall. Correlation-based feature selection for machine learning, 1998. PhD thesis, Department of Computer Science, University of Waikato, Hamilto, New Zealand.
  7. V.N. Vapnik, 1995. The Natural of Statistical Learning Theory. Springer.
  8. C.J.C. Burges, 1998. A tutorial on support vector machines for pattern recognition. DataMining and Knowledge Discovery, 2(2):121-167.
  9. M.P. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares, Jr.,and Haussler D, 2000. Knowledge-based analysis of microarray gene expression data usingsupport vector machines. Proceedings of the National Academy of Science, 97(1):262-267.
  10. B. Scholkop and A.J. Smola, 2002. Learning with Kernels. MIT Press, Cambridge, MA.
  11. A. Zien, G. Raetsch, S. Mika, B. Schoelkopf, C. Lemmen, A. Smola, T. Lengauer, and K.-R, 2000. Mueller. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics, 16(9):799-807.
  12. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik, 2001. Feature selection for SVMs. Advances in Neural Information Processing Systems, 13:668-674.
  13. J. Platt., 1998. Fast training of support vector machines using sequential minimal optimization. In B. Schlkopf, C. Burges, and Smola A., editors, Advances in Kernel Methods - Support Vector Learning, pp. 185-208. MIT Press.
  14. F. Zeng, H.C. Yap, and L. Wong, 2002. Using feature generation and feature selection for accurate prediction of translation initiation sites. Proceedings of 13th International Conference on Genome Informatics, pages 192-200, Tokyo, Japan.
  15. H. Liu and L. Wong, 2003. Data mining tools for biological sequences. Journal of Bioinformatics and Computational Biology, 1(1):139-168.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 150-157
Manuscript Number : CSEIT172521
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. T. Shanmuga Vadivu , "Patient Survival Prediction Using Data Mining Technique", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.150-157, September-October.2017
URL : http://ijsrcseit.com/CSEIT172521

Follow Us

Contact Us