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.
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.
Index Terms — Kidney Stone Disease, Multilayer Perceptrons, Radial Basis Function Networks, Learning Vector Quantization, Diagnosis
I. Introduction
Kidney stones disease is becoming more and more common now days. Kidney stones are created when certain substances in urine including calcium, oxalate, and sometimes uric acid crystallize. These minerals and salts form crystals, which can then join together and form a kidney stone. Each type of kidney stone has a different cause1. Stones are classified according to their chemical composition. Approximately 80% of all kidneys stones are calcium oxalate stones, which are the most problematic. The formation of these stones may be caused by genetic factors and also depends upon age, and geographical factors2. However, more important are dietary and lifestyle factors, and the results of acquired metabolic defects leading to crystal formation and growth of a kidney stone3. In2 authors give a detail explanation regarding what are kidney stones, its Types and different symptoms of this disease. In3 authors describe the different factors like age, sex, race, body weight, ethnicity which may cause of kidney stones.
The most common problem in the Field of automatic diagnostic is the diagnostics using fast and accurate algorithm which doesn’t require long time to run and give accurate and correct results4. To reduce the diagnosis time and improve the diagnosis accuracy, it has become more of a demanding issue to develop reliable and powerful medical diagnosis system to support the yet and still increasingly complicated diagnosis decision process. The medical diagnosis by nature is a complex and fuzzy cognitive Process hence soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical diagnosis. In disease diagnosis the learning and detection of partial disease can be helpful when time and information constraints are present. Thus artificial neural networks provide a good means to partial diagnosis.
This we used three neural networks algorithms for measuring their classification accuracy against time taken to classify for diagnosis purpose. This paper is thus organized as following in section II a brief introduction of the artificial neural network, in section III Previous related work works that had been done, in section IV kidney stone data set that is used in this research has been discussed, in section V artificial neural networks classifiers used is described, In section VI simulation tool used is described, in section VII experiment results and discussion is given and in the last section we conclude the paper.
II. Artificial Neural Networks Introduction
Artificial neural networks (ANN) have emerged as a result of simulation of biological nervous system, such as the brain on a computer. Artificial Neural networks are represented as a set of nodes called neurons and connections between them. The connections have weights associated with them, representing the “strength” of those connections. Nowadays neural networks can be applied to problems that do not have algorithmic solutions or problems for which algorithmic solutions are too complex to be found. In others words the kind of problems in which inputs and outputs variables does not have a clear relationship between them, a neural networks is a efficient approach in such problems. Most neural network architecture has three layers in its structure. First layer is input layer which provides an interface with the environment, second layer is hidden layer where computation is done and last layer is output layer where output is stored. Data is propagated through successive layers, with the final result available at the “output layer”. Many different types of neural networks are available and multi layer neural networks are the most popular. MLP popularity is due to more then one hidden layer in its structure which helps sometimes in solving complex problems which a single hidden layer neural network cannot solve.
Figure 1 show multilayer perceptron structure with N number of inputs neurons corresponding to N number of hidden and output neurons.
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Figure 1: Multilayer Perceptron architecture
Nowadays artificial neural network has become most widely tool used for diagnosis of diseases. Because of the Fault tolerance, Generalization and Learning from environment like capabilities of Artificial neural networks it is becoming more and more popular in medical diagnosis and many more others areas. One of the network structures that have been widely used is the feed forward network where network connections are allowed only between the nodes in one layer and those in the next layer. Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected person.
Figure 2 shows the architecture of feed forward neural networks (MLP) for decision making. In this structure three inputs are provided to network and then inputs and weights are summed by using summation function. Finally output is in binary form either Yes or No. Yes for a patient who is suffering from disease and no for unaffected person. In this work three neural network algorithms LVQ, RBF and feed forward architecture with back propagation algorithms have been investigated for diagnosis of kidney stones for early detection of disease. All three algorithms are compared against their classification accuracy to classify affected and unaffected persons.
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Figure 2 Feed Forward Architecture in Decision Making
III. Related Work
There is a continuous study and research going on in this field of medical diagnosis. There is lot of work has been done on diseases like Cancer, Diabetes, Heart attack etc using neural networks. Koizumi.N et al (2011) presents a” Robust kidney stone tracking for a non- invasive ultrasound theragnostic system” propose a non- invasive ultrasound theragnostic system that tracks movement in an affected area (kidney stones, in the present study) by irradiating the area with highintensity focused ultrasound5. Mitri F.G. (2011) presents “Vibro-acoustography imaging of kidney stones in vitro Vibro-acoustography” (VA) is an ultrasound-based modality sensitive to stiffness and free from speckle and possesses some advantages over conventional ultrasound imaging in terms of image quality6. Duryeal A.P . et al. (2010) presents an “Optimization of Histotripsy for Kidney Stone Erosion Histotripsy” is a technique for the mechanical fractionation of tissue structures which utilizes focused pulsed-ultrasound to direct the activity of a cavitational bubble cloud7. Rouhani M et al. (2009) present the “Comparison of several Ann architecture RBF, GRNN, PNN, LVQ and SVM on Thyroid Disease”. The performance of each architecture is studied, and the best method is selected for each of classification tasks. In this paper RBF and PNN selected best models for diagnosis8. Shukla A. et al (2009) presents” Knowledge Based Approach for Diagnosis of Breast cancer” this paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast cancer using Ann and apply three neural networks algorithms BPA, RBF and LVQ on the disease and find best model for diagnosis. In this paper LVQ select as a best model for diagnosis on the disease and find best model for diagnosis9.
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1 http://www.webmd.com/kidneystones/understandi ng- 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/.htm l
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
- Quote paper
- Koushal Kumar (Author), B. Abhishek (Author), 2012, Artificial Neural Networks for Diagnosis of Kidney Stones Disease, Munich, GRIN Verlag, https://www.grin.com/document/196640
