General Framework for Biomedical Knowledge With Data Mining Techniques
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - May Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-5 | ||
| Year of Publication : 2013 | ||
| Authors :B.Madasamy, Dr.J.Jebamalar Tamilselvi |
B.Madasamy, Dr.J.Jebamalar Tamilselvi"General Framework for Biomedical Knowledge With Data Mining Techniques "International Journal of Computer Trends and Technology (IJCTT),V4(5):1485-1491 May Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - Data mining is the process which automates the extraction of predictive information discovers the interesting knowledge from large amounts of data stored in information repositories. Biomedical informatics (BMI) is the science underlying acquisition, maintenance, retrieval, collecting, manipulating, and analysing the biomedical knowledge and information to improve medical data analysis, problem solving, and decision making, inspired by efforts toward progress in medical domain. In this research work a comprehensive framework will be generated which comprises of various data mining techniques and evaluate meaningful information from biomedical data. Data mining field will be applied to biomedical data to analyze the characteristics, identify patterns of interest, for diagnosing and predicting patients` health. These proposed biomedical data mining framework useful to the scholars who are interested in the related researches of data mining and medical domain.
References-
[1] James Gardner, Li Xing “An integrated framework for de-identifying unstructured medical data” Data & Knowledge Engineering www.elsevier.com/locate/datak
[2] Wan-Shiou Yang, San-Yih HwangW.-S. Yang, S.-Y. Hwang “A process-mining framework for the detection of healthcare fraud and abuse” Expert Systems with Applications 31 (2006) 56–68
[3] Latha .K1 Kalimuthu.S2 Dr.Rajaram.R3 “Information Extraction from Biomedical Literature using Text Mining Framework” International Journal of Imaging Science and Engineering (IJISE)
[4] Ramkishore Bhattacharyya “Cohesion: A concept and framework for confident association discovery with potential application in microarray mining” Applied Soft Computing 11 (2011) 592–604 journal homepage: www.elsevier.com/locate/asoc
[5] Riyaz Sikora, Selwyn Piramuthu “Computing, Artificial Intelligence and Information Management Framework for efficient feature selection in genetic algorithm based data mining” European Journal of Operational Research 180 (2007) 723– 737
[6] George Hripcsak, Suzanne Bakken, Peter D. Stetson, and mla L. Patel, “Mining complex clinical data for patient safety research: a framework for event discovery” Journal of Biomedical Informatics 36 (2003) 120–13
[7] Pearson WR (2000) “Flexible sequence similarity searching with the FASTA3 program package” Methods Mol. Biol. 132:185–219
[8] The Genome International Sequencing Consortium (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921
[9] Ramos-Pollan, R., et al., “Exploiting eInfrastructures for medical image storage and analysis: A Grid application for mammography CAD,” in The Seventh IASTED International Conference on Biomedical Engineering. Austria: Innsbruck, 2010
Keywords — Data mining, Biomedical, Framework, Knowledge Discovery.