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Poornima, B.
- An Improved Decision Tree Classification for Breast Cancer Detection with Optimal Parameters
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1 Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, IN
1 Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, IN
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Journal of Applied Information Science, Vol 9, No 1 (2021), Pagination: 19-21Abstract
The development of proficient and successful decision trees stays a key theme in machine learning on account of their effortlessness and adaptability. A great deal of heuristic calculations has been proposed to build close ideal choice trees. The traditional decision tree calculations and the split measures they utilized are entropy, Gain Ratio and Gini list individually. In this paper, we introduced a conventional correlation of the conduct of two of the most well-known split capacities, to be specific the Gini Index and entropy. The target of this paper is to distinguish and investigate these imperative standards’ or elements of decision tree calculation for Wisconsin Breast cancer growth expectation. The significant commitment of this examination work is to give a way to choose a particular parting factor for the development of decision tree calculation according to necessity or issue. Trial results indicated that utilizing the decision tree calculation with the entropy parting technique accomplished higher grouping precision than Gini list strategy.Keywords
Breast Cancer, Data Mining, Decision Tree, Entropy, Gini.References
- American Institute for Cancer Research, New Global Cancer Data: GLOBOCAN 2018 UICC, 2018. [Online]. Available: https://www.uicc.org/news/new-global-canc er-data-globocan-2018
- C. Wilson, S. Tobin, R. Young, “The exploding worldwide cancer burden,” International Journal of Gynecological Cancer, vol. 14, no. 1, pp. 1-11, 2004.
- D. M. Parkin, F. Bray, J. F. Ferlay, and P. Pisani, “Global cancer statistics, 2002,” CA - A Cancer Journal for Clinicians, vol. 55, no. 2, pp. 74-108, 2005.
- G. R. Kumar, G. A. Ramachandra, and K. Nagamani, “An efficient prediction of breast cancer data using data mining techniques,” International Journal of Innovations in Engineering and Technology (IJIET), vol. 2, no. 4, pp. 138-144, ISSN: 2319-1058, Aug. 2013.
- G. R. Kumar, V. S. Kongara, and G. A. Ramachandra, “An efficient ensemble based classification techniques for medical diagnosis,” International Journal of Latest Technology in Engineering, Management and Applied Sciences, vol. 2, no. 8, pp. 5-9, ISSN: 2278-2540, Aug. 2013.
- https://www.uptodate.com/contents/breast-cancer-guide-todiagnosis-and-treatment-beyond-the-basics (Accessed 07-01-2017).
- I. H. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., San Francisco: Morgan Kaufmann, 2005.
- J. Han, and M. Kamber, “Data mining concepts and techniques,” The Morgan Kaufmann series in Data Management Systems, 2nd ed., San Mateo, CA: Morgan Kaufmann, 2006.
- C. Laronga, A. B. Chagpar, and S. R. Vora, “Patient education: Breast cancer guide to diagnosis and treatment,” 2016.
- National Cancer Institute, “Financial burden of cancer care,” Cancer Trends Progress Report, 2018. [Online]. Available: https://progressreport.cancer.gov/after/economic_burden
- UCI Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/ml/