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Breast Cancer Detection using SVM Classifier with Grid Search Technique

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Authors:
Vishal Deshwal, Mukta Sharma
10.5120/ijca2019919157

Vishal Deshwal and Mukta Sharma. Breast Cancer Detection using SVM Classifier with Grid Search Technique. International Journal of Computer Applications 178(31):18-23, July 2019. BibTeX

@article{10.5120/ijca2019919157,
	author = {Vishal Deshwal and Mukta Sharma},
	title = {Breast Cancer Detection using SVM Classifier with Grid Search Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2019},
	volume = {178},
	number = {31},
	month = {Jul},
	year = {2019},
	issn = {0975-8887},
	pages = {18-23},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume178/number31/30735-2019919157},
	doi = {10.5120/ijca2019919157},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Medical science is a boon to mankind. The technological advancement has widened the scope of curing and fighting with the diseases. It is essential to diagnose the symptoms and identify the disease timely. It has been observed that breast cancer cases are the most reported cases among women around the world and the second most common cancer overall. According to [1] World Cancer Research Fund International, London has shared that there were over 2 million new cases in 2018. In the year 2012, the BCRF (Breast Cancer Research Foundation) has reported nearly 1.7 million new breast cancer cases [2]. With the help of technology, medical science is trying to predict cancer; which can significantly increase the chances of survival. In this research paper, the authors have illustrated the model to predict breast cancer with Support Vector Machine using Grid search. First Support vector machine model is tested without a grid search. Later, Support vector machine model is tested with grid search. Finally, the comparative analysis was done and based on the result; a new model was built. The new model designed is based on grid search on data before fitting it for prediction, which enhances the outcome.

References

  1. Bemmel J.H.V. (1984). The structure of medical informatics. Med Inform. 9(3-4). Pp. 175–180.
  2. Shortliffe, E.H., Blois, M.S. (2006). The computer meets medicine and biology: the emergence of a discipline. In: Shortliffe EH, editor. Biomedical informatics: computer applications in health care and biomedicine. Springer Science+ Business Media, LLC; New York, NY: pp. 3–45.
  3. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, in press Retrieved from: https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics
  4. Breast Cancer Research Foundation (n.a), Breast Cancer Statistics, Retrieved From: https://www.bcrf.org/breast-cancer-statistics
  5. Pierce, B.G. and Wallace, C. (1971). University of Colorado Medical Center, Denver, Cancer Research. Retrieved From: http://cancerres.aacrjournals.org/content/canres/31/2/127.full.pdf
  6. Wolberg, H.W., Street, N.W., & Mangasarian, L.O., (n.a). Breast Cancer Wisconsin (Diagnostic) Data Set. UCI Machine learning Repository. Retrieved From:
  7. Theodoros, E., & Pontil, M. (2001). Support Vector Machines: Theory and Applications. 2049. 249-257. 10.1007/3-540-44673-7_12. Retrieved From: https://www.researchgate.net/publication/221621494_Support_Vector_Machines_Theory_and_Applications.
  8. Visa, S & Ramsay, B. & Ralescu, A. & Knaap, E. (2011). Confusion Matrix-based Feature Selection. CEUR Workshop Proceedings. 710. 120-127.
  9. Retrieved From: https://www.researchgate.net/publication/220833270_Confusion_Matrix-based_Feature_Selection
  10. Lameski, P. & Zdravevski, E. & Mingov, R. & Kulakov, A. (2015). SVM Parameter Tuning with Grid Search and Its Impact on Reduction of Model Over-fitting. 10.1007/978-3-319-25783-9_41. Retrieved From: https://www.researchgate.net/publication/284188795_SVM_Parameter_Tuning_with_Grid_Search_and_Its_Impact_on_Reduction_of_Model_Over-fitting

Keywords

Machine learning; support vector machine; grid search; cancer prediction