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.
Table of Contents
I. Introduction
II. Artificial Neural Networks Introduction
III. Related Work
IV. Kidney Stone Dataset
V. Artificial Neural Networks Classifiers Used
A) Radial Basis Function Networks
B) Multilayer Perceptron with BPA
C) Learning Vector Quantization Algorithm
VI. Simulation Tool Used
VII. Experiment Results and Discussion
VIII. Conclusion
Research Objectives and Themes
The primary objective of this research is to evaluate and compare the classification performance of three distinct neural network algorithms—Multilayer Perceptron (MLP) with back propagation, Radial Basis Function (RBF), and Learning Vector Quantization (LVQ)—to enhance the accuracy and speed of medical diagnosis for kidney stone disease.
- Comparison of neural network architectures (MLP, RBF, LVQ) for medical diagnostic purposes.
- Evaluation of model classification accuracy based on real-world patient datasets.
- Optimization of computational efficiency in diagnostic time and data processing.
- Application of the Waikato Environment for Knowledge Analysis (WEKA) as a simulation framework.
- Identification of the most effective algorithm for early kidney stone detection.
Excerpt from the Book
V. Artificial Neural Networks Classifiers Used
The main aim of this work is to compare the classification performance of three different neural networks algorithms in diagnose of kidney stones disease. The algorithms used are Radial basis functions (RBF), Learning Vector Quantization (LVQ) and a Multilayer perceptron with back propagation algorithm. A brief description of them is as follow.
A) Radial Basis Function Networks
A Radial basis function (RBF) network is a special type of neural network that uses a radial basis function as its activation function. A Radial Basis Function (RBF) neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain radial basis transfer functions whose outputs are inversely proportional to the distance from the center of the neuron [10]. RBF networks are very popular for function approximation, curve fitting, Time Series prediction, and control problems. Because of more compact topology then other neural networks and faster learning speed, RBF networks have attracted considerable attention and they have been widely applied in many science and engineering fields [11-14].
A general block diagram of an RBF network is illustrated in Figure 3. In RBF networks, the outputs of the input layer are determined by calculating the distance between the network inputs and hidden layer centers. The second layer is the linear hidden layer and outputs of this layer are weighted forms of the input layer outputs. Each neuron of the hidden layer has a parameter vector called center.
Summary of Chapters
I. Introduction: Presents the prevalence of kidney stone disease and the motivation for using soft computing methods like artificial neural networks for automated diagnosis.
II. Artificial Neural Networks Introduction: Provides a foundational overview of ANN architecture, including input, hidden, and output layers, and explains their application in solving complex, non-linear problems.
III. Related Work: Surveys existing research in medical diagnostics using ultrasound imaging and various neural network architectures for disease classification.
IV. Kidney Stone Dataset: Details the composition of the real-world dataset, consisting of 1000 instances and 8 attributes representing clinical patient symptoms.
V. Artificial Neural Networks Classifiers Used: Describes the specific neural network models employed: Radial Basis Function (RBF), Multilayer Perceptron (MLP) with Back Propagation, and Learning Vector Quantization (LVQ).
VI. Simulation Tool Used: Introduces WEKA 3.7.5 as the open-source software environment used for data preprocessing, training, and testing.
VII. Experiment Results and Discussion: Analyzes the performance metrics of the three algorithms, focusing on accuracy, error rates, and classification speed.
VIII. Conclusion: Summarizes findings, identifying the Multilayer Perceptron as the most effective algorithm for diagnosing kidney stones with 92% accuracy.
Keywords
Artificial Neural Networks, Kidney Stone Disease, Multilayer Perceptron, Radial Basis Function, Learning Vector Quantization, Medical Diagnosis, Back Propagation, WEKA, Soft Computing, Pattern Classification, Healthcare Informatics, Diagnostic Accuracy, Clinical Data Analysis.
Frequently Asked Questions
What is the core focus of this research paper?
The paper focuses on leveraging artificial neural networks to improve the accuracy and speed of medical diagnosis for kidney stone patients.
What are the primary thematic areas covered in this study?
The study covers artificial neural network architectures, medical diagnostic systems, clinical data analysis, and performance comparison of machine learning algorithms.
What is the main research question or objective?
The primary objective is to determine which neural network algorithm (MLP, RBF, or LVQ) provides the best classification performance for kidney stone diagnosis.
Which scientific methodology is applied in this work?
The authors employ a comparative experimental approach, training three different neural network models on a real-world dataset of 1000 patient instances using the WEKA simulation tool.
What topics are discussed in the main body of the paper?
The main body covers the fundamentals of ANN, a review of related medical diagnostic research, details on the kidney stone dataset, descriptions of the three selected neural network models, and an analysis of experimental results.
Which keywords best characterize this work?
Key terms include Artificial Neural Networks, Kidney Stone Disease, Multilayer Perceptron, Medical Diagnosis, and WEKA simulation.
Why did the authors choose the Multilayer Perceptron (MLP) over other models?
The experiments demonstrated that the Multilayer Perceptron achieved the highest classification accuracy of 92% and performed more reliably than RBF and LVQ for this specific dataset.
How was the dataset for this research obtained and structured?
The data was collected from medical laboratories and contains 1000 instances with 7 symptoms as attributes, categorized into two classes (Yes/No for disease presence).
What role does the WEKA simulation tool play in this study?
WEKA serves as the computational platform for applying neural network algorithms, managing data preprocessing, and calculating performance metrics like kappa statistics and error rates.
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- Koushal Kumar (Autor:in), B. Abhishek (Autor:in), 2012, Artificial Neural Networks for Diagnosis of Kidney Stones Disease, München, GRIN Verlag, https://www.grin.com/document/196640
