International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 4, No. 7, 8 Jul. 2012
Cover page and Table of Contents: PDF (size: 323KB)
Artificial Neural Networks for Diagnosis of Kidney Stones Disease
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Author(s)
Koushal Kumar
1,*
Abhishek
1
Index Terms
Kidney Stone Disease, Multilayer Perceptrons, Radial Basis Function Networks, Learning Vector Quantization, Diagnosis
Abstract
Artificial Neural networks are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. They have several advantages over parametric classifiers such as discriminate analysis. The objective of this paper is to diagnose kidney stone disease by using three different neural network algorithms which have different architecture and characteristics. The aim of this work is to compare the performance of all three neural networks on the basis of its accuracy, time taken to build model, and training data set size. We will use Learning vector quantization (LVQ), two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF) networks for diagnosis of kidney stone disease. In this work we used Waikato Environment for Knowledge Analysis (WEKA) version 3.7.5 as simulation tool which is an open source tool. The data set we used for diagnosis is real world data with 1000 instances and 8 attributes. In the end part we check the performance comparison of different algorithms to propose the best algorithm for kidney stone diagnosis. So this will helps in early identification of kidney stone in patients and reduces the diagnosis time.
Cite This Paper
Koushal Kumar, Abhishek, "Artificial Neural Networks for Diagnosis of Kidney Stones Disease", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.7, pp.20-25, 2012. DOI:10.5815/ijitcs.2012.07.03
Reference
[1]http://www.webmd.com/kidneystones/understanding- kidney-stones-basics
[2]Moe OW. Kidney stones: pathophysiology & Medical management. Lancet 2006; 367: 333–44.
[3]Sandhya A et al “Kidney Stone Disease Etiology And Evaluation” Institute of Genetics and Hospital for Genetic Diseases, India International Journal of Applied Biology and Pharmaceutical Technology,may june 2010
[4]http://www.internage.kicv.ua/projects/neuraln/.html
[5]Koizumi N et al “Robust Kidney Stone Tracking for a Non-invasive Ultrasound” Shanghai International Conference Center May 9-13, 2011, Shanghai, China
[6]Mitri F.G. “Vibro-acoustography imaging of kidney stones in vitro” IEEE Transactions on Biomedical Engineering 2011
[7]Duryeal A.P. et al. “Optimization of Histotripsy for Kidney Stone EROSION Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 2Department of Urology, University of Michigan, Ann Arbor, MI 2010
[8]Rouhani M. et al. “The comparison of several ANN Architecture on thyroid disease”IslamiAzadUniversity, Gonabad branch Gonabad 2010
[9]Shukla A. et al “Diagnosis of Thyroid Disorders using Artificial Neural Networks” Department of Information Communication and Technology, ABV-Indian Institute of Information Technology and Management Gwalior, India IEEE International Advance Computing Conference 2009
[10]Broomhead, D; Low, D. Multivariable functional interpolation and adaptive networks. Complex Systems 1988, 2, 321–355.
[11]Tuba Kurban and Erkan Beşdok. “A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification” Geomatics Engineering, Engineering Faculty, Erciyes University, Turkey 2009
[12]Devaraj, D.; Yegnanarayana, B.; Ramar, K. Radial basis function networks for fast contingency ranking. Electric. Power Energy Syst. 2002, 24, 387–395.
[13]Fu, X. Wang, L. Data dimensionality reduction with application to simplifying rbf network structure and improving classification performance. IEEE Trans. Syst. Man Cybern. Part B 2003,33, 399–409.
[14]Han, M.; Xi, J. Efficient clustering of radial basis perceptron neural network for pattern recognition. Pattern Recognit 2004, 37, 2059–2067.
[15]Rohitash chandra, Kaylash Chaudhary and akshay kumar.,2007 The combination and comparison of neural networks with decision trees for wine classification. school of sciences and technology.
[16]Jamal M. Nazzal, Ibrahim M. El-Emary and Salam A. Najim.” Multilayer Perceptron Neural Network (MLPs) For Analyzing the Properties of Jordan Oil Shale. World Applied Sciences Journal 5 (5): 546-552, 2008
[17]Koushal Kumar, Gour Sundar Mitra Thakur. ”Extracting Explanation from Artificial Neural Networks” International Journal of Computer Science and Information Technologies, Vol.3 (2) 2012, 3812- 3815.
[18]Modjtaba Rouhani and Mehdi motavalli haghighi. “The Diagnosis of Hepatitis diseases by Support Vector Machines and Artificial Neural Networks.International Association of Computer Science and Information Technology - Spring Conference 2009
[19]WEKA at http://www.cs.waikato.ac.nz/~ml/weka.
[20]http://www.dmi.columbia.edu/homepages/chuangj/kappa