COMBINED METHODOLOGY of the CLASSIFICATION RULES for MEDICAL DATA - SETS
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2012 by IJETT Journal | ||
| Volume-3 Issue-1 |
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| Year of Publication : 2012 | ||
| Authors : V.Sneha Latha , P.Y.L.Swetha , M.Bhavya , G. Geetha , D. K.Suhasini |
Citation
V.Sneha Latha , P.Y.L.Swetha , M.Bhavya , G. Geetha , D. K.Suhasini. "COMBINED METHODOLOGY of the CLASSIFICATION RULES for MEDICAL DATA - SETS". International Journal of Engineering Trends and Technology (IJETT). V3(1):32-36 Jan-Feb 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
‘Data mining’ offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help prac titioners in making clinical decisions. The purpose of this study was to build a hybrid data mining model to extract classification knowledge for various health hazards to aid in clinical decisions in an emergency department. This study utilized real world data collected from an emergenc y department of a hospital and used a new model which is developed combining the Apriori algorithm and a C5.0 algorithm to generate a classification rule base for the classification of medical data - sets, which can help physi cians to make clinical decisions faster and more accurately
References
[1] “ A Hybrid Data Mining Method for the Medical Classification of Chest Pain ”authored by Sung Ho Ha and Seong Hyeon Joo
[2] “ Performance Evaluation of Decision Tree Classifiers on Medical Datasets ” authored by D.Lavanya Dr. K.Usha Rani Research Scholar Dept. of Computer Science Sri Padmavathi Mahila Visvavidyalayam
[3] “ Medical Domain Knowledge and Associative Classification Rules in Diagnosis” authored by Sung Ho Ha, Kyungpook National University, Korea
[4] “ Apriori Algorithm Review for Finals. ” SE 157B, Spring Semester 2007 Professor Lee By Gaurang Negandhi
[5] http://en.wikipedia.org/wiki/C4.5_algorithm
[6] http://rulequest.com/see5 - comparison.html .
[7] “ Application of Data Mining Technique for Diagnosis of Posterior Uveal Melanoma” authored by Darius JEGELEVI ? CIUS, Ar ?unas LUKOŠEVI ? CIUS Institute of Biomedical Engineering, Kaunas University of Technology Alvydas PAUNKSNIS, Valerijus BARZDŽIUKAS Department of Ophthalmology, Institute for Biomedical Research
[8] http://ascelibrary.org/teo/resource/1/jtpedi/v13 6/i4/p332_s1
[9] http://storm.cis.fordham.edu/~gweiss/resources.ht ml
[10] Jiawei Han and Micheline Kamber Data Mining: Concepts and Techniques, 2 nd ed . The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Publishers , March 200 6. ISBN 1 -
[11]W. T. Lin, S. T. Wang, T. C. Chiang, Y. X. Shi, W. Y. Chen, and H. M. Chen, “Abnormal diagnosis of Emergency Department triage explored with data mining technology: An Emergency Department at a Medical Center in Taiwan taken as an example”, Expert Systems with Applications , vol. 37, pp. 2733 - 2741, 2010.
[12]C. Duguary, and F. Chetouane, “Modeling and improving emergency department systems using discrete event simulation,” Simulation , vol. 83,
Keywords
Data mining, chest pain, clinical decisions, emergency department, Hybrid model.