Breast cancer diagnosis using least square support vector machine

Authors:
Kemal Polat;Salih Güneş
Affiliations:
Electrical and Electronics Engineering Department, Selcuk University, 42075 Konya, Turkey;Electrical and Electronics Engineering Department, Selcuk University, 42075 Konya, Turkey
Venue:
Digital Signal Processing
Year:
2007

Citing 5
Cited 22

The nature of statistical learning theory

The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Supervised fuzzy clustering for the identification of fuzzy classifiers

Pattern Recognition Letters
Fuzzy least squares support vector machines for multiclass problems

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research

Support vector machines combined with feature selection for breast cancer diagnosis

Expert Systems with Applications: An International Journal
Statistical analysis of mammographic features and its classification using support vector machine

Expert Systems with Applications: An International Journal
A decision making system to automatic recognize of traffic accidents on the basis of a GIS platform

Expert Systems with Applications: An International Journal
Effective framework for prediction of disease outcome using medical datasets: clustering and classification

International Journal of Computational Intelligence Studies
Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes

Expert Systems with Applications: An International Journal
A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis

Expert Systems with Applications: An International Journal
Breast cancer classification applying artificial metaplasticity algorithm

Neurocomputing
WBCD breast cancer database classification applying artificial metaplasticity neural network

Expert Systems with Applications: An International Journal
Classification of parkinsonian and essential tremor using empirical mode decomposition and support vector machine

Digital Signal Processing
Subtractive clustering attribute weighting (SCAW) to discriminate the traffic accidents on Konya-Afyonkarahisar highway in Turkey with the help of GIS: A case study

Advances in Engineering Software
Risk prediction for postoperative morbidity of endovascular aneurysm repair using ensemble model

ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images

Journal of Medical Systems
Diagnosis of Several Diseases by Using Combined Kernels with Support Vector Machine

Journal of Medical Systems
Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques

Journal of Medical Systems
A fast and adaptive automated disease diagnosis method with an innovative neural network model

Neural Networks
An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features

Journal of Medical Systems
Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Letters: A novel online adaptive kernel method with kernel centers determined by a support vector regression approach

Neurocomputing
Extended fuzzy c-means: an analyzing data clustering problems

Cluster Computing
Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms

Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend

Expert Systems with Applications: An International Journal
Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds

Digital Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. In this paper, breast cancer diagnosis was conducted using least square support vector machine (LS-SVM) classifier algorithm. The robustness of the LS-SVM is examined using classification accuracy, analysis of sensitivity and specificity, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 98.53% and it is very promising compared to the previously reported classification techniques. Consequently, by LS-SVM, the obtained results show that the used method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.