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Research Article
Breast Cancer Detection Using Supervised Machine Learning Algorithms
K. Packiyalakshmi1
A. Karmehala22
1Department of Computer Science, Sri Kaliswari College (Autonomous), Tamilnadu, India. 2Assistant professor, Department of Computer Sccience, Sri Kaliswari College (Autonomous), Tamilnadu, India.
Published Online: March-April 2023
Pages: 471-475
Cite this article
No DOIReferences
1. Shubham Sharma, Archit Aggarwal, Tanupriya Choudhury “Breast Cancer Detection using Machine Learning Algorithms"
2. National Institute of Cancer Prevention and Research, cancer statistics [Online], Available: http://cancerindia.org.in/statistics/
3. WHO breast cancer statistics [Online]. Available: http://www.who.int/cancer/prevention/diagnosis-screening/breastcancer/en/
4. B.M.Gayathri and C.P.Sumathi,”Mamdani fuzzy inference system for breast cancer risk detection”, 2015.
5. Mohd,F.,Thomas,M, “Comparison of different classification techniques using WEKA for Breast cancer” 2007.
6. Time complexity and optimality of kNN [Online] Available: https://nlp.stanford.edu/IR-book/html/htmledition/time-complexityandoptimality-of-knn-1.html
7. Gilles Louppe, “Understanding Random Forests from theory to practice” 2015.
2. National Institute of Cancer Prevention and Research, cancer statistics [Online], Available: http://cancerindia.org.in/statistics/
3. WHO breast cancer statistics [Online]. Available: http://www.who.int/cancer/prevention/diagnosis-screening/breastcancer/en/
4. B.M.Gayathri and C.P.Sumathi,”Mamdani fuzzy inference system for breast cancer risk detection”, 2015.
5. Mohd,F.,Thomas,M, “Comparison of different classification techniques using WEKA for Breast cancer” 2007.
6. Time complexity and optimality of kNN [Online] Available: https://nlp.stanford.edu/IR-book/html/htmledition/time-complexityandoptimality-of-knn-1.html
7. Gilles Louppe, “Understanding Random Forests from theory to practice” 2015.
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