Home   >   CSC-OpenAccess Library   >    Manuscript Information
Implementation of Back-Propagation Algorithm for Renal Datamining
S Sai, P.Thrimurthy, S.Purushothaman
Pages - 35 - 47     |    Revised - 15-04-2008     |    Published - 30-04-2008
Volume - 2   Issue - 2    |    Publication Date - April 2008  Table of Contents
MORE INFORMATION
KEYWORDS
ABSTRACT
The present medical era data mining place a important role for quick access of appropriate information. To achieve this full automation is required which means less human interference. Therefore automatic renal data mining with decision making algorithm is necessary. Renal failure contributes to major health problem. In this research work a distributed neural network has been applied to a data mining problem for classification of renal data to have for proper diagnosis of patient. A multi layer perceptron with back propagation algorithm has been used. The network was trained offline using 500 patterns each of 17 inputs. Using the weight obtained during training, fresh patterns were tested for accuracy of diagnosis.
CITED BY (4)  
1 Medialdea, J. L. G., Manamparan, M. E. C., Sorita, M. G. M., Ponce, E. L., & Beltran Jr, A. A. A Novel Thermal Gas Analyzer Using Adaptive Neuro-Fuzzy Inference System (ANFIS).
2 Raju, R., Devakumaran, K., Balasubramanian, A., & Balan, G. (2012). A composite model that overwhelms outliers to anticipate the stages in renal disorder. Computer Science & Engineering, 2(3), 1.
3 Basak, T. K., Ramanujam, T., Jeybalan, S., Bhatt, M., Garg, D., & Garg, R. (2011). Growth Factor Inhibiting PKC Sensor in E-coli Environment Using Classification Technique and ANN Method. Sensors & Transducers, 126(3), 125.
4 Mustare, N. B. (2011). A Study of Conservative Physiological Homeostasis and its Analysis Linked to Status of E. Coli Environment Related to Cancer Disease.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 iSEEK 
5 Socol@r  
6 ResearchGATE 
7 Libsearch 
8 Bielefeld Academic Search Engine (BASE) 
9 Scribd 
10 WorldCat 
11 SlideShare 
12 PDFCAST 
13 PdfSR 
14 Chinese Directory Of Open Access 
Adam E. Gaweda, Alfred A. Jacobs and Michael E. Brie (2003), Artificial Neural Network-based Pharmacodynamic Population Analysis in Chronic Renal Failure, IEEE Tans, pp 71-74
Altman, D. 1991. Practical Statistics for Medical Research, Chapman and hall.
Bernard Widrow (1990), 30 Years of adaptive neural networks: Perceptron, madaline and back-propagation, Proc. of the IEEE, 18(9), pp 1415 - 1442.
Fortuna L, Graziani S, LoPresti M and Muscato G (1992), Improving back propagation learning using auxiliary neural networks, Int. J of Cont. , 55(4), pp 793-807.
Hirose Y, Yamashita K Y and Hijiya S (1991), Back-propagation algorithm which varies the number of hidden units, Neural Networks, 4, pp 61-66.
Hush D R and Horne B G (1993), Progress in supervised neural networks, IEEE Signal Proc. Mag., pp 8-38.
Kleinbaum, D.G., Kupper ,L.L.(eds.) 1982. Epidemiologic Research: Principles and Quantitative Methods, John Wiley &: Sons, New York.
Lippmann R P (1987), An introduction to computing with neural nets, IEEE Trans. On Acoustics, Speech and Signal Processing Magazine, V35, N4, pp.4.-22
M. S. Sousa, M. L. Q. Mattoso and N. F. F. Ebecken.(1998). Data Mining: A Database Perspective. COPPE, Federal University of Rio de Janeiro, pp.1-19.
Manal Abdel Wahed, Khaled Wahba (2004), Data Mining Based-Assistant Tools for Physicians to Diagnose Diseases ,IEEE Trans, pp 388-391.
Ming-Syan Chen, Jiawei Han and Philip S. Yu. Data Mining: An Overview From a Database Perspective. IEEE Transactions on Knowledge and Data Engineering, Vol. 8(6), December 1996, pp. 866-883.
Pena-Mora, F. & Hussein, K. 1998, Interaction Dynamics in Collaborative Civil Engineering Design Discourse: Applications in Computer Mediated Communication.Journal of Computer Aided Civil and Infrastructure Engineering, Vol. 14, pp. 171-185
Shih-Chi Huang and Yih-Fang Haung (1990), Learning algorithms for perceptrons using back-propagation with selective updates, IEEE Cont. Sys. Mag., pp 56-
Themistoklis Palpanas. Knowledge Discovery in Data Warehouses. ACM Sigmod Record. vol. 29(3), September 2000, pp. 88- 100.
Tsumoto, S. G5: Medzcine, In: Kloesgen, W. and Zytkow, J. (eds.) Handbook of Knowledge Dicovery and Data Mining.
Usama Fayyad. 1997, Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases. Proceedings of the 9th International Conference on Scientific and Statistical Database Management (SSDBM '97). Olympia, WA, pp. 2-11.
Usama M. Fayyad. Data Mining and Knowledge Discovery: Making Sense Out of Data. IEEE Expert, October 1996, pp. 20-25.
Van Bemme1,J. and Musen, M. A.1997. Handbook of Medical Informatics, Springer-Verlag, New York.
Y Shahar & MAMusen, 'Knowledge-based Temporal Abstractioin in Clinical Domains' Artif. Intell. In Med. 8, 1996, pp.267-298.
Yao Y L and Fang X D (1993), Assessment of chip forming patterns with tool wear progression in machining via neural networks, Int.J. Mach. Tools & Mfg, 33 (1), pp 89 -102.
Mr. S Sai
Dept of MCA Hindu College PG Courses - India
Mr. P.Thrimurthy
Dept. of Computer Science & Engg. - India
Dr. S.Purushothaman
Sun College of Engineering and Technology - India
dr.s.purushothaman@gmail.com


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS