Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
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
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Review: Using support vector machines in diagnoses of urological dysfunctions
Expert Systems with Applications: An International Journal
A novel estimation of the regularization parameter for Ɛ-SVM
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
MammoSys: A content-based image retrieval system using breast density patterns
Computer Methods and Programs in Biomedicine
Breast cancer diagnosis using WNN based on GA
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Computers in Biology and Medicine
Short term wind speed prediction based on evolutionary support vector regression algorithms
Expert Systems with Applications: An International Journal
Designing simulated annealing and subtractive clustering based fuzzy classifier
Applied Soft Computing
Expert Systems with Applications: An International Journal
WBCD breast cancer database classification applying artificial metaplasticity neural network
Expert Systems with Applications: An International Journal
Multi-parametric gaussian kernel function optimization for ε-SVMr using a genetic algorithm
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Purging false negatives in cancer diagnosis using incremental active learning
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Expert Systems with Applications: An International Journal
Gene selection and PSO-BP classifier encoding a prior information
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
A supervised method for microcalcification cluster diagnosis
Integrated Computer-Aided Engineering
Hi-index | 12.07 |
Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the support vector machines (SVM) have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on a SVM-based method combined with feature selection has been proposed. Experiments have been conducted on different training-test partitions of the Wisconsin breast cancer dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, positive and negative predictive values, receiver operating characteristic (ROC) curves and confusion matrix. The results show that the highest classification accuracy (99.51%) is obtained for the SVM model that contains five features, and this is very promising compared to the previously reported results.