Browse > Article
http://dx.doi.org/10.5307/JBE.2008.33.5.340

Classification of UTI Using RBF and LVQ Artificial Neural Network in Urine Dipstick Screening Test  

Min, Kyoung-Kee (Dept. of Biomechatronic Engineering, Sungkyunkwan University)
Kang, Myung-Seo (Dept. of Laboratory Medicine, College of Medicine, Pochon CHA University)
Shin, Ki-Young (Dept. of Biomechatronic Engineering, Sungkyunkwan University)
Lee, Sang-Sik (Dept. of Biomechatronic Engineering, Sungkyunkwan University)
Hun, Joung-Hwan (Dept. of Biomechatronic Engineering, Sungkyunkwan University)
Publication Information
Journal of Biosystems Engineering / v.33, no.5, 2008 , pp. 340-347 More about this Journal
Abstract
Dipstick urinalysis is used as a routine test for a screening test of UTI (urinary tract infection) in primary practice because urine dipstick test is simple. The result of dipstick urinalysis brings medical professionals to make a microscopic examination and urine culture for exact UTI diagnosis, therefore it is emphasized on a role of screening test. The objective of this study was to the classification between UTI patients and normal subjects using hybrid neural network classifier with enhanced clustering performance in urine dipstick screening test. In order to propose a classifier, we made a hybrid neural network which combines with RBF layer, summation & normalization layer and L VQ artificial neural network layer. For the demonstration of proposed hybrid neural network, we compared proposed classifier with various artificial neural networks such as back-propagation, RBFNN and PNN method. As a result, classification performance of proposed classifier was able to classify 95.81% of the normal subjects and 83.87% of the UTI patients, total average 90.72% according to validation dataset. The proposed classifier confirms better performance than other classifiers. Therefore the application of such a proposed classifier expect to utilize telemedicine to classify between UTI patients and normal subjects in the future.
Keywords
RBF-LVQ hybrid artificial neural network; UTI classification; Dipstick urinalysis; Neural network classifier;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Wigton, R. S., V. L. Hoellerich. J. P. Ornato. V. Leu, L. A. Mazzotta and I. H. C. Chen. 1985. Use of clinical findings in the diagnosis of urinary tract infection in women. Arch. Intern. Med. 145(12):2222-2227   DOI
2 Wilson, J. D. 1991. Harrison' Principles of Internal Medicine. 12th edition, Vol. 2, McGRAW-HILL, International Edition, New York, USA
3 Sultana, R. V., S. Zalstein. P. Cameron and D. Campbell. 2001. Dipstick urinalysis and the accuracy of the clinical diagnosis of urinary tract infection. J. of Emergency Medicine 20(1): 13-19   DOI   ScienceOn
4 Shin, S. S., S. B. Lee and Y. H. Cho. 2001. Recognition of disease in medical image. J. of Contents Association 1(1): 8-14   과학기술학회마을
5 Lisboa, P. J. and F. G. Taktak. 2006. The use of artificial neural networks in decision support in cancer: A systematic review. Neural Networks 19(4):408-415   DOI   ScienceOn
6 Oh, C. S. 1996. Neuro Computer. Jeesung Press, Seoul, Korea
7 Lee, G. S. 2006. Study on the Management System for Occupational Disease and Injury of Farmers. Seoul National University Doctor Thesis
8 Leibovici, L., A. Gershon, A. Laor, O. Kalter-Leibovici and Y. Danon. 1989. A clinical model for diagnosis of urinary tract infection in young women, Arch. Intern. Med. 149(9): 2048-2050   DOI
9 Boniatis, I., L. Costaridou, D. Cavouras, I. Kalatzis, E. Panagiotopoulos and G. Panayiotakis. 2007. Assessing hip osteoarthritis severity utilizing a probabilistic neural network based classification scheme. Medical Engineering & Physics 29(2): 227-237   DOI   ScienceOn
10 Choi, S. D., H. J. Cho, D. Y. Cho, B. Y. Yu and K. H. Kim. 2000. Diagnostic value of dipstick urinalysis as a screening test for urinary tract infection. J. Korean Acad. Fam. Med. 21(6):772-781
11 Bae, S. I., H. C. Lee, S. Y. Lim, K. D. Kim and B. C. Jung. 2000. Cytocentrifuge gram stain method and urine dipstick test as a screening test of bacteriuria. Korean J. Clin. Pathol. 20(4):410-414
12 Jeon, G. R., G. R. Kim, S. Y. Ye, C. H. Kim, D. U. Jeong and J. H. Cho. 2003. A study on the design of classifier for urine analysis system. J. of KOSOMBE 24(3):193-201
13 Li, Y. C., L. Li, W. T. Chiu and W. S. Jian. 2000. Neural network modeling for surgical decisions on traumatic brain injury patients. Int. J. Med. Inform. 57(1):1-9   DOI   ScienceOn
14 Heckerling, P. S., G. J. Canaris, S. D. Flach, T. G. Tape, R. S. Wigton and B. S. Gerber. 2007. Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. Int. J. of Medical Informatics 76(4):289-296   DOI   ScienceOn
15 Hines, J. W. 1997. MATLAB Supplement to Fuzzy and Neural Approaches in Engineering. John Wiley and Sons, New York, USA
16 Huang, D. S. 1999. Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recogn. Artif. Intell. 13(7):1083-1101   DOI
17 Bent, S., B. K. Nallamothu, D. L. Simel, S. D. Fihn and S. Saint. 2002. Does this women have an acute uncomplicated urinary tract infection?. JAMA 287(20):2701-2710   DOI   ScienceOn
18 Kim, K. D., S. H. Koo, E. C. Kim, J. M. Kim, J. H. Kim, J. Q. Kim, H. J. Kim, D. S. Moon, W. K. Min, K. Y. Soo, Y. L., J. J. Lee, C. H. Jeon. M. E. Cho and S. S. Cho. 2006. Annual report on external quality assessment in clinical chemistry in Korea. J. Lab. Med. Qual. Assur. 28(1):63-89
19 Little, P., S. Turner, K. Rumsby, G. Warner, M. Moore, J. Lowes, H. Smith, C. Hawke and M. Mullee. 2006. Developing clinical rules to predict urinary tract infection in primary care settings: sensitivity and specificity of near patient tests (dipsticks) and clinical scores. British Journal of General Practice 56(529): 606-612