An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis
Mahua Nandy. Article: An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis. IJCA Proceedings on Computing Communication and Sensor Network 2013 CCSN 2013(2):1-4, December 2013. Full text available. BibTeX
@article{key:article,
author = {Mahua Nandy},
title = {Article: An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis},
journal = {IJCA Proceedings on Computing Communication and Sensor Network 2013},
year = {2013},
volume = {CCSN 2013},
number = {2},
pages = {1-4},
month = {December},
note = {Full text available}
}
Abstract
In this work, ANN and SVM, two most popular supervised machine learning techniques, are considered as the representatives and k-means clustering is used as representative of unsupervised learning. By analyzing the diagnosis result using Wisconsin Breast Cancer Dataset (WBCD) which is commonly used among researchers who use machine learning methods for breast cancer diagnosis, it can be concluded that SVM outperforms in case of breast cancer diagnosis. The result is verified using two other breast cancer datasets. One is Breast Cancer Dataset from UCI Machine Learning Repository and another one is "Breast cancer dataset with Electrical Impedance Measurements in samples of freshly excised tissue". The purpose of the comparison is to choose the best solution in terms of performance. Another notable significance of the work is that accuracy of the recognition drops down severely if proper feature set is not used. One significant disadvantage of neural network is its time taken to build the model which is also evident from the work.
References
- T. Justin, R. Gajsek, V. Struc, Dobrisek, " Comparison of different classification methods for emotion recognition" S. MIPRO, 2010 Proceedings of the 33rd International Convention , pp. 700 -770.
- I. Mironica, R. Dogaru, "A comparison between various classification methods for image classification stage in CBIR", Interntional Symposium on Signals, Circuits and Systems (ISSCS), 2011 10th, pp. 1-4.
- Boyu Wang , Chi Man Wong, Feng Wan, Peng Un Mak, Pui In Mak, Mang I Vai, "Comparison of different classification methods for EEG-based brain computer interfaces: A case study", International Conference on Information and Automation, 2009. ICIA '09, pp. 1416 – 1421.
- Mehmet Fatih Akay, "Support vector machines combined with feature selection for breast cancer diagnosis", Expert Systems with Applications: An International Journal, volume 36 Issue 2, March, 2009 pp. 3240-3247.
- Lee, N. Gobert, Fujita, Hiroshi, "K-means Clustering for Classifying Unlabelled MRI Data", Digital Image Computing Techniques and Applications, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image, 3-5 Dec, 2007, pp. 92-98.
- J. Jiang, P. Trundle, J. Ren, "Medical image analysis with artificial neural networks", Journal of Computerized Medical Imaging and Graphics vol. 34, issue 8 (2010) pp. 617–631.
- Dataset:http://archive. ics. uci. edu/ml/datasets/breast+tissue
- Dataset:http://archive. ics. uci. edu/ml/machine-learning- databases/breast-cancer-wisconsin/breast-cancer-wisconsin. data
- Dataset:http://archive. ics. uci. edu/ml/machine-learning-databases/breast-cancer/