Browse > Article
http://dx.doi.org/10.9718/JBER.2010.31.1.040

Rule Weight-Based Fuzzy Classification Model for Analyzing Admission-Discharge of Dyspnea Patients  

Son, Chang-Sik (Biomedical Informatics Technology Center, Keimyung Univ.)
Shin, A-Mi (Dept. of Medical Informatics, School of Medicine, Keimyung Univ.)
Lee, Young-Dong (Biomedical Informatics Technology Center, Keimyung Univ.)
Park, Hyoung-Seob (Interventional Cardiology Dept. of Internal Medicine, Keimyung Univ.)
Park, Hee-Joon (Dept. of Medical Informatics, School of Medicine, Keimyung Univ.)
Kim, Yoon-Nyun (Interventional Cardiology Dept. of Internal Medicine, Keimyung Univ.)
Publication Information
Journal of Biomedical Engineering Research / v.31, no.1, 2010 , pp. 40-49 More about this Journal
Abstract
A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.
Keywords
Fuzzy Classification Model; Rule Weight; Rule Generation; Dyspnea Patient;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 G. Scano and N. Ambrosino, "Pathophysiology of dyspnea," Lug, vol. 180, p.131, 2002.
2 E.J. Cha, T.S. Lee, Y.S. Whang, J.W. Kim, S.O. Yang, K.H. Jung, and H.K. Ryu, "Automated clinical test result analysis system-Application to liver function test," J. Biomed. Eng. Res., vol. 14, no. 4, pp.341-348, 1993.
3 K.R. Jun, S.J. Lee, B.C. Choi, S.H. An, K. Ha, J.Y. Kim, and J.H. Kim, "A study on the development of urine analysis system using strip and evaluation of experimental result by means of fuzzy inference," J. Biomed. Eng. Res., vol. 19, no. 5, pp.477-486, 1998.
4 J.H. Hwang, K.S. Park, and B.G. Min, "A study on the detection of pulmonary blood vessel using pyramid images and fuzzy theory," J. Biomed. Eng. Res., vol. 12, no. 2, pp.99-105, 1991.
5 O.K. Yoon, H.S. Kim, D.M. Kwak, B.S. Kim, D.W. Kim, W.M. Pyun, and K.H. Park, "Segmentation of multispectral MRI using fuzzy clustering," J. Biomed. Eng. Res., vol. 21, no. 4, pp.333-338, 2000.
6 U.C. Yoon, J.W. Hwang, J.S. Kim, J.J. Kim, I.Y. Kim, J.S. Kwon, and S.I. Kim, "Successive fuzzy classification and improved parcellation method for brain analysis," J. Biomed. Eng. Res., vol. 22, no. 5, pp.377-383, 2001.
7 H. Ishibuchi and T. Nakashima, "Effect of rule weights in fuzzy rule-based classification systems," IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp.506-515, 2001.   DOI   ScienceOn
8 E.G. Mansoori, M.J. Zolghadri, and S.D. Katebi, "A weighting function for improving fuzzy classification systems performance," Fuzzy Sets and Syst., vol. 158, no. 5, pp.583-591, 2007.   DOI   ScienceOn
9 H. Ishibuchi, T. Nakashima, and T. Murata, "Three-objective genetics-based machine learning for linguistic rule extraction," Inform. Sci., vol. 136, no. 1-4, pp.109-133, 2001.   DOI   ScienceOn
10 Y.C. Tsai, C.H. Cheng, and J.R. Chang, "Entropy-based fuzzy rough classification approach for extracting classification rules," Experts Syst. with Appl., vol. 31, no. 2, pp.436-443, 2006.   DOI   ScienceOn
11 P. Jevon and B. Ewens, "Assessment of a breathless patient," Nursing, vol. 15, no. 16, pp.48-55, 2001.
12 M.B. Parshall, "Psycometric characteristics of dyspnea descriptor ratings in emergency department patients with exacerbation chronic obstruction pulmonary disease," Research in Nursing & Health, vol. 25, pp.331-344, 2002.   DOI   ScienceOn
13 J.R. Quinlan, C4.5: Programs for machine learning, Elsevier Science Ltd, 1992.
14 C.G. Yu, "Differential diagnosis of dyspnea, Tuberculosis and Respiratory Diseases", vol. 55, no. 1, pp.5-14, 2003.   DOI
15 T.H. Kim, "Differential diagnosis and treatment of dyspnea," Korean J. Med., vol. 76, no. 4, pp.425-430, 2009.
16 L. Zadeh, "Fuzzy sets," Inform. and Contr., vol. 8, pp.338-353, 1965.   DOI
17 Y. Chen, B. Yang, A. Abraham, and L. Peng, "Automatic design of hierarchical takagi-sugeno type fuzzy systems using evolutionary algorithms," IEEE Trans. Fuzzy Syst., vol. 15, no. 3, pp.385-397, 2007.   DOI
18 C.S. Son, H.M. Chung, and S.H. Kwon, "Selection method of fuzzy partitions in fuzzy rule-based classification systems," J. Korean Institute of Intelligent Syst., vol. 18, no. 3, pp.360-366, 2008.   과학기술학회마을   DOI
19 J.H. Friedman, "Regularized discriminant analysis," J. American Statistical Association, vol. 84, no. 405, pp.165-175, 1989.   DOI   ScienceOn
20 T.M. Cover and P.E. Hart, "Nearest neighbor pattern classification," IEEE Trans. Inform. Theory, vol. 13, no. 1, pp.21-27, 1967.   DOI
21 C. Cortes and V. Vapnik, "Support-vector networks," Machine Learn., vol. 20, no. 3, pp.273-297, 1995.