Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

Authors:
Javad Salimi Sartakhti;Mohammad Hossein Zangooei;Kourosh Mozafari
Affiliations:
SCS Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Terhran, Iran;SCS Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Terhran, Iran;SCS Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Terhran, Iran
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 18
Cited 5

Support-Vector Networks

Machine Learning
Principles of Neurocomputing for Science and Engineering

Principles of Neurocomputing for Science and Engineering
The Case against Accuracy Estimation for Comparing Induction Algorithms

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Ensembles of Partitions via Data Resampling

ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C

Journal of Medical Systems
Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation

Digital Signal Processing
Medical decision support system based on artificial immune recognition immune system (AIRS), fuzzy weighted pre-processing and feature selection

Expert Systems with Applications: An International Journal
A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS

Computer Methods and Programs in Biomedicine
A comparative study on thyroid disease diagnosis using neural networks

Expert Systems with Applications: An International Journal
Using support vector regression to model the correlation between the clinical metastases time and gene expression profile for breast cancer

Artificial Intelligence in Medicine
A hybrid evolutionary algorithm for attribute selection in data mining

Expert Systems with Applications: An International Journal
Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System

Expert Systems with Applications: An International Journal
A penalized likelihood based pattern classification algorithm

Pattern Recognition
Predicting breast cancer survivability: a comparison of three data mining methods

Artificial Intelligence in Medicine
ACO-based hybrid classification system with feature subset selection and model parameters optimization

Neurocomputing
Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C

Artificial Intelligence in Medicine
Short communication: A new intelligent hepatitis diagnosis system: PCA-LSSVM

Expert Systems with Applications: An International Journal
A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm

Journal of Medical Systems

Hybrid real-coded genetic algorithm and MIMO CMAC NN classifier for solving medical data classification problems

BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo

Computer Methods and Programs in Biomedicine
Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

Computer Methods and Programs in Biomedicine
Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

Computer Methods and Programs in Biomedicine
A new hybrid intelligent system for accurate detection of Parkinson's disease

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, diagnosis of hepatitis disease, which is a very common and important disease, is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA). Simulated annealing is a stochastic method currently in wide use for difficult optimization problems. Intensively explored support vector machine due to its several unique advantages is successfully verified as a predicting method in recent years. We take the dataset used in our study from the UCI machine learning database. The classification accuracy is obtained via 10-fold cross validation. The obtained classification accuracy of our method is 96.25% and it is very promising with regard to the other classification methods in the literature for this problem.