Journal of Biosystems Engineering

  • 제33권5호
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  • Pages.340-347
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  • 2008
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  • 1738-1266(pISSN)
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  • 2234-1862(eISSN)
한국농업기계학회 (Korean Society for Agricultural Machinery)
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RBF와 LVQ 인공신경망을 이용한 요(尿) 딥스틱 선별검사에서의 요로감염 분류

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)
  • 발행 : 2008.10.25
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초록

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.

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참고문헌

  1. 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
  2. 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 https://doi.org/10.1001/jama.287.20.2701
  3. 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 https://doi.org/10.1016/j.medengphy.2006.03.003
  4. 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
  5. 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 https://doi.org/10.1016/j.ijmedinf.2006.01.005
  6. Hines, J. W. 1997. MATLAB Supplement to Fuzzy and Neural Approaches in Engineering. John Wiley and Sons, New York, USA
  7. Huang, D. S. 1999. Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recogn. Artif. Intell. 13(7):1083-1101 https://doi.org/10.1142/S0218001499000604
  8. 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
  9. 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
  10. Lee, G. S. 2006. Study on the Management System for Occupational Disease and Injury of Farmers. Seoul National University Doctor Thesis
  11. 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 https://doi.org/10.1001/archinte.149.9.2048
  12. 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 https://doi.org/10.1016/S1386-5056(99)00054-4
  13. 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
  14. 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 https://doi.org/10.1016/j.neunet.2005.10.007
  15. Oh, C. S. 1996. Neuro Computer. Jeesung Press, Seoul, Korea
  16. 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 https://doi.org/10.1016/S0736-4679(00)00290-0
  17. 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
  18. 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 https://doi.org/10.1001/archinte.145.12.2222
  19. Wilson, J. D. 1991. Harrison' Principles of Internal Medicine. 12th edition, Vol. 2, McGRAW-HILL, International Edition, New York, USA