Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Universal approximation using radial-basis-function networks
Neural Computation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
BIONET: an artificial neural network model for diagnosis of diseases
Pattern Recognition Letters
Time-series forecasting using GA-tuned radial basis functions
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Journal of Global Optimization
Medical Analysis and Diagnosis by Neural Networks
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Automatic basis selection techniques for RBF networks
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
A differential evolution based incremental training method for RBF networks
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Training RBF Networks Using a DE Algorithm with Adaptive Control
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Expert Systems with Applications: An International Journal
Fast learning in networks of locally-tuned processing units
Neural Computation
Journal of Medical Systems
A comparative study on thyroid disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Classification of audio signals using SVM and RBFNN
Expert Systems with Applications: An International Journal
Breast mass classification based on cytological patterns using RBFNN and SVM
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Expert Systems with Applications: An International Journal
Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis
Artificial Intelligence in Medicine
Case-based reasoning support for liver disease diagnosis
Artificial Intelligence in Medicine
Heart Disease Classification Using Neural Network and Feature Selection
ICSENG '11 Proceedings of the 2011 21st International Conference on Systems Engineering
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Confidentiality issues for medical data miners
Artificial Intelligence in Medicine
Data mining a diabetic data warehouse
Artificial Intelligence in Medicine
Selecting radial basis function network centers with recursive orthogonal least squares training
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
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The classification of diseases appears as one of the fundamental problems for a medical practitioner, which might be substantially improved by intelligent systems. The present work is aimed at designing in what way an intelligent system supporting medical decision can be developed by hybridizing radial basis function neural networks RBFNs and differential evolution DE. To this extent, a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one, differential evolution is used to reveal the parameters of the modified kernel. The second phase focus on optimization of weights for learning the networks. The proposed method is validated using five medical datasets such as bupa liver disorders, pima Indians diabetes, new thyroid, stalog heart, and hepatitis. In addition, a predefined set of basis functions are considered to gain insight into, which basis function is better for what kind of domain through an empirical analysis. The experiment results indicate that the proposed method classification accuracy with 95% and 98% confidence interval is better than the base line classifier i.e., simple RBFNs in all aforementioned datasets. In the case of imbalanced dataset like new thyroid, the authors have noted that with 98% confidence level the classification accuracy of the proposed method based on the multi-quadratic kernel is better than other kernels; however, in the case of hepatitis, the proposed method based on cubic kernel is promising.