Application of Data Mining Techniques in Early Detection of Breast Cancer

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2018 by IJETT Journal
Volume-56 Number-1
Year of Publication : 2018
Authors : F.Leenavinmalar,Dr.A.Kumarkombaiya
DOI :  10.14445/22315381/IJETT-V56P208

Citation 

F.Leenavinmalar,Dr.A.Kumarkombaiya "Application of Data Mining Techniques in Early Detection of Breast Cancer", International Journal of Engineering Trends and Technology (IJETT), V56(1),43-45 February 2018. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Cancer is a class of diseases characterized by out-of-control cell growth. There are over 100 different types of cancer, and each is classified by the type of cell that is initially affected[1]. Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer, early detection of cancer helps the patients to prevent from vulnerability and get cured. Data mining and machine learning technique are used widely in medical sciences in identifying, diagnosing, diseases. In this paper we are proposing possible data mining techniques in early detection of breast cancer, Wisconsin breast cancer data set is used for experiments, and are evaluated using sensitivity, specificity andclassification accuracy.

Reference
[1] American Cancer Society. Breast Cancer Facts & Figures 2005-2006. Atlanta: American Cancer Society, Inc. (http://www.cancer.org/)
[2] G. Ravi Kumar, Dr. G. A. Ramachandra, K.Nagamani, “ An Efficient Prediction of Breast Cancer Data using Data Mining Techniques”, International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 Issue 4 August 2013.
[3] Breast Cancer Wisconsin Data [online]. Available: http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancerwisconsin/breast-cancer-wisconsin.
[4] Weka: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/
[5] Brenner, H., Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. Lancet. 360:1131–1135, 2002.
[6] Witten H.I., Frank E., Data Mining: Practical Machine Learning Tools and Techniques, Second edition, Morgan Kaufmann Publishers, 2005.
[7] D. Delen, G. Walker and A. Kadam (2005), Predicting breast cancer survivability: a comparison of three data mining methods, Artificial Intelligence in Medicine, vol.34, pp.113-127.
[8] Y Rejani- “Early detection of breast cancerusing SVM”. 2009 –arxiv
[9] Ilias Maglogiannis, E Zafiropoulos “An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers” Applied Intelligence, 2009 –Springer.
[10] Ian H. Witten and Eibe Frank. Data Mining:Practical machine learning tools and techniques, 2nd Edition. San Fransisco:Morgan Kaufmann; 2005.

keywords
Breast cancer survivability, data mining, Wisconsin breast cancer data set, SVM, C5.0, cancer prediction techniques.