Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.1.125

Penalized logistic regression models for determining the discharge of dyspnea patients  

Park, Cheolyong (Department of Statistics, Keimyung University)
Kye, Myo Jin (Department of Statistics, Keimyung University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.1, 2013 , pp. 125-133 More about this Journal
Abstract
In this paper, penalized binary logistic regression models are employed as statistical models for determining the discharge of 668 patients with a chief complaint of dyspnea based on 11 blood tests results. Specifically, the ridge model based on $L^2$ penalty and the Lasso model based on $L^1$ penalty are considered in this paper. In the comparison of prediction accuracy, our models are compared with the logistic regression models with all 11 explanatory variables and the selected variables by variable selection method. The results show that the prediction accuracy of the ridge logistic regression model is the best among 4 models based on 10-fold cross-validation.
Keywords
Cross-validation; determining discharge; dyspnea patients; penalized logistic regression;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Fayyad, U. M. and Irani, K. B. (1993). Multi-interval discretization of continuous attributes as prepro¬cessing for classification learning. Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1022-1027.
2 Friedman, J., Hastie, T. and Tibshirani, R. (2008). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1-22.
3 Lee, S., Park, J. E. and Oh, K. W. (2003) Discretization of continuous-valued attributes considering data distribution. Journal of Korea Fuzzy Logic and Intelligent Systems Society, 13, 391-396.   DOI   ScienceOn
4 McCullagh, P. and Nelder, J. A. (1989). Generalized linear models, 2nd Ed., Chapman and Hall, London.
5 Na, J. H., Kim, J. M. and Cho, W. S. (2005). Comparison of binary discretization algorithms for data mining. Journal of the Korean Data & Information Science Society, 16, 769-780.
6 Park, C. (2011). A quantification study of blood test results for dyspnea patients. Journal of the Korean Data & Information Science Society, 22, 477-485.
7 Park, C., Kim, T. Y., Kwon, O. J. and Park, H. S. (2010). A simple statistical model for determining the admission or discharge of dyspnea patients. Journal of the Korean Data & Information Science Society, 21, 279-289.
8 Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B, 21, 279-289.
9 Whitten, D. A. and Tibshirani, R. (2011). Penalized classification using Fisher's linear discriminant. Jour-nal of the Royal Statistical Society B, 73, 753-772.   DOI   ScienceOn
10 Johnson, R. A. and Wichern, D. W. (1992). Applied multivariate statistical analysis, 3rd Ed., Prentice Hall, New Jersey.
11 Kerber, R. (1992). ChiMerge: Discretization of numeric attribute. Proceedings of the 10th National Conference on Artificial Intelligence (AAAI-92), 123-127.
12 Kim, J. S., Jang, Y. M. and Na, J. H. (2005) Comparison of multiway discretization algorithms for data mining. Journal of the Korean Data & Information Science Society, 16, 801-813.