Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Intelligent target recognition based on wavelet packet neural network
Expert Systems with Applications: An International Journal
An incremental SVM for intrusion detection based on key feature selection
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An incremental genetic algorithm for classification and sensitivity analysis of its parameters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Association rule mining to detect factors which contribute to heart disease in males and females
Expert Systems with Applications: An International Journal
The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo
Computer Methods and Programs in Biomedicine
Automated detecting and classifying of sleep apnea syndrome based on genetic-SVM
International Journal of Hybrid Intelligent Systems
| Hi-index | 12.06 |
In this study, an intelligent system based on genetic-support vector machines (GSVM) approach is presented for classification of the Doppler signals of the heart valve diseases. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound. GSVM is used in this study for diagnosis of the heart valve diseases. The GSVM selects of most appropriate wavelet filter type for problem, wavelet entropy parameter, the optimal kernel function type, kernel function parameter, and soft margin constant C penalty parameter of support vector machines (SVM) classifier. The performance of the GSVM system proposed in this study is evaluated in 215 samples. The test results show that this GSVM system is effective to detect Doppler heart sounds. The averaged rate of correct classification rate was about 95%.