Machine learning approaches for cancer detection
Автор: Ayush Sharma, Sudhanshu Kulshrestha, Sibi B. Daniel
Журнал: International Journal of Engineering and Manufacturing @ijem
Статья в выпуске: 2 vol.8, 2018 года.
Бесплатный доступ
Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.
Support Vector Machine, Artificial Neural Network, Cancer, Accuracy, Machine Learning
Короткий адрес: https://sciup.org/15015842
IDR: 15015842 | DOI: 10.5815/ijem.2018.02.05
Список литературы Machine learning approaches for cancer detection
- National Cancer Institute (NCI), Cancer – A Comprehensive Guide http://cancer.gov/about-cancer, Accesed on March, 2016.
- Konstantina Kourou, Themis P. Exarchos, Michalis V. Karamouzis, Machine learning applications in cancer prognosis and prediction, accessed on August, 2016.Author(s) Profile
- Ahrim Youn, Richard Simon, Identifying cancer driver genes in tumour genome sequencing studies, Bioinformatics. 2011 Jan 15; 27(2): 175–181.
- UCI Machine Learning Repository of data sets for machine learning, http://archive.ics.uci.edu/ml/, Accessed at July, 2016.
- Data Catlog, Nation Cancer Institute R&D Resources, https://www.cancer.gov/research/resources/data-catalog, Accessed July 2016.
- Ismail Saritas, Prediction of Breast Cancer using Artificial Neural Networks, Springer Science+Business Media, LLC 2011, Published on 12 August, 2011.
- AS Ren J. 2012. ANN vs. SVM: Which One Performs Better in Classification of MCCs in Mammogram Imaging. Knowledge-Based Systems. 26: 144–153. 10 (1), January 2012.
- I.S. Subashini T. S., V. Ramalingam, and S. Palanivel. 2009. Breast Mass Classification Based on Cytological Patterns using RBFNN and SVM. Expert Systems with Applications. 36: 5284–5290.
- Ojha, Varun Kumar; Abraham, Ajith; Snášel, Václav (2017-04-01). "Metaheuristic design of feedforward neural networks: A review of two decades of research".
- Bottaci, Leonardo. "Artificial Neural Networks Applied to Outcome Prediction for Colorectal Cancer Patients in Separate Institutions".
- Cortes, C.; Vapnik, V. (1995). "Support-vector networks". Machine Learning. 20 (3): 273–297.
- O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
- William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196
- A Pär Stattin, Sigrid Carlsson, Benny Holmström, Andrew Vickers, Jonas Hugosson, Hans Lilja, Håkan Jonsson, Pro Prostate Cancer Mortality in Areas With High and Low Prostate Cancer Incidence https://academic.oup.com/jnci/article/doi/10.1093/jnci/dju007/1745564/Prostate-Cancer-Mortality-in-Areas-With-High-and.
- Liu Y., and Y. F. Zheng. 2004. FS_SFS: A Novel Feature Selection Method for Support Vector Machines. IEEE International Conference on Acoustic, Speech, and Signal Processing. 5: 797–800.
- Keyvanfard F., M. A. Shoorehdeli, and M. Teshnehlab. 2011. Feature Selection and Classification of Breast Cancer on Dynamic Magnetic Resonance Imaging using ANN and SVM. American Journal of Biomedical Engineering. 1: 20–25.
- Subashini T. S., V. Ramalingam, and S. Palanivel. 2009. Breast Mass Classification Based on Cytological Patterns using RBFNN and SVM. Expert Systems with Applications. 36: 5284–5290.
- Ren J. 2012. ANN vs. SVM: Which One Performs Better in Classification of MCCs in Mammogram Imaging. Knowledge-Based Systems. 26: 144–153.
- Altman, N. S. (1992). "An introduction to kernel and nearest-neighbour nonparametric regression". The American Statistician. 46 (3): 175–185.
