A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

Authors:
Emre Çomak;Ahmet Arslan
Affiliations:
Department of Computer Engineering, Pamukkale University, Denizli, Turkey 20020;Department of Computer Engineering, Selcuk University, Konya, Turkey 42075
Venue:
Journal of Medical Systems
Year:
2012

Citing 13
Cited 0

Wavelet applications in medicine

IEEE Spectrum
SVM binary classifier ensembles for image classification

Proceedings of the tenth international conference on Information and knowledge management
Information Theoretic Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Fuzzy least squares support vector machines for multiclass problems

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Support vector clustering

The Journal of Machine Learning Research
2005 Special Issue: Bayesian approach to feature selection and parameter tuning for support vector machine classifiers

Neural Networks - 2005 Special issue: IJCNN 2005
Efficient optimization of support vector machine learning parameters for unbalanced datasets

Journal of Computational and Applied Mathematics
A decision support system based on support vector machines for diagnosis of the heart valve diseases

Computers in Biology and Medicine
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases

Pattern Recognition Letters
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

Expert Systems with Applications: An International Journal
Particle swarm optimization for parameter determination and feature selection of support vector machines

Expert Systems with Applications: An International Journal
A new neural network for cluster-detection-and-labeling

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Classification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and 驴). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.