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.

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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.
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