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Diagnosis of Breast Cancer using Decision Tree Data Mining Technique

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 98 - Number 10
Year of Publication: 2014
Authors:
Ronak Sumbaly
N. Vishnusri
S. Jeyalatha
10.5120/17219-7456

Ronak Sumbaly, N Vishnusri and S Jeyalatha. Article: Diagnosis of Breast Cancer using Decision Tree Data Mining Technique. International Journal of Computer Applications 98(10):16-24, July 2014. Full text available. BibTeX

@article{key:article,
	author = {Ronak Sumbaly and N. Vishnusri and S. Jeyalatha},
	title = {Article: Diagnosis of Breast Cancer using Decision Tree Data Mining Technique},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {98},
	number = {10},
	pages = {16-24},
	month = {July},
	note = {Full text available}
}

Abstract

Cancer is a big issue all around the world. It is a disease, which is fatal in many cases and has affected the lives of many and will continue to affect the lives of many more. Breast cancer represents the second primary cause of cancer deaths in women today and has become the most common cancer among women both in the developed and the developing world in the last years. 40,000 women die in a year from this disease, which is one woman every 13 minute dying from this disease everyday. Early detection of breast cancer is far easier to cure. This paper presents a decision tree based data mining technique for early detection of breast cancer. Breast cancer diagnosis differentiates benign (lacks ability to invade neighboring tissue) from malignant (ability to invade neighboring tissue) breast tumors. This paper also discusses various data mining approaches that have been utilized for breast cancer diagnosis, and also summarizes breast cancer in general (types, risk factors, symptoms and treatment).

References

  • National Cancer Institute: http://www. cancer. gov/cancertopics/types/breast.
  • National Cancer Institute Breast Cancer, http://www. cancer. gov/cancertopics/types/breast
  • Breast Cancer Organization, http://www. breastcancer. org/ symptoms/types
  • Breast Cancer Organization, http://www. breastcancer. org/ risk/factors/
  • Breast Cancer Organization, http://www. breastcancer. org/symptoms/
  • Bellaachia Abdelghani and Erhan Guven,"Predicting Breast Cancer Survivability using Data Mining Techniques", Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining, 06.
  • J. Han and M. Kamber, "Data Mining Concepts and Techniques", Morgan Kauffman Publishers, 2000.
  • Neeraj Bhargava, Girja Sharma, Ritu Bhargava and Manish Mathuria, Decision Tree Analysis on J48 Algorithm for Data Mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013.
  • H. Blockeel and J. Struyf. Efficient algorithms for decision tree cross-validation. Proceedings of the Eighteenth International Conference on Machine Learning (C. Brodley and A. Danyluk, eds. ), Morgan Kaufmann, 2001, pp. 11-18
  • William H Wolberg, Olvi Mangasarian, UCI Machine Learning Repository [http://archive. ics. uci. edu/ml]. Irvine, CA
  • White, A. P. , Liu, W. Z. : Technical note: Bias in information-based measures in decision tree induction. Machine Learning 15(3), 321–329 (1994)
  • Chi C. L. , Street W. H. and Wolberg W. H. , "Application of Artifical Neural Network- based Survival Analysis on Two Breast Cancer Datasets", Annual Symposium Proceedings / AMIA Symposium, 2007.
  • D. Brazokovic and M. Neskovic. Mammogram screening using multiresolution-based image segmen- tation. International Journal of Pattern Recognition and Artificial Intelligence, 7(6):1437–1460, 1993.
  • R. Agrawal, T. Imielinski, and A. Swami. Min- ing association rules between sets of items in large databases. In Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data, pages 207–216, Washington, D. C. , May 1993.
  • Bellaachia Abdelghani and Erhan Guven, "Predicting Breast Cancer Survivability using Data Mining Techniques, "Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining," 2006.
  • Michael Feld, Dr. Michael Kipp, Dr. Alassane Ndiaye and Dr. Dominik Heckmann "Weka: Practical machine learning tools and techniques with Java implementations"
  • Wikipedia, http://en. wikipedia. org/wiki/File:Mammo_breast_cancer. jpg
  • American Cancer Society, http://www. cancer. org/cancer/breastcancer/detailedguide/breast-cancer-diagnosis
  • NHS Choices, http://www. nhs. uk/Conditions/Cancer-of-the-breast-female/Pages/Treatment. aspx