Total views : 191

Using Frequent Itemset Mining for Breast Cancer Detection


  • Jamia Millia Islamia, New Delhi - 110025, Delhi, India


Objectives: This paper introduces an improvement over the use of Artificial Neural Networks (ANN) for breast cancer detection. Methods: It suggests use of frequent pattern mining for minimizing the dimensions of breast cancer database. After reduction step, the database is then input to an ANN for classification. Findings: We have shown through experimentation that the proposed model not only reduces the input database dimensions but also produces better classification results. Application: The proposed model will be highly beneficial in the field of medicine and will help in precise detection of breast cancer.


Artificial Neural Network, Association Rule Mining, Breast Cancer Detection, Dimension Reduction, Frequent Itemset Mining

Full Text:

 |  (PDF views: 183)


  • World cancer report 2014. World Health Organization; 2014.
  • Chou SM, Lee TS, Shao YE, Chenb IF. Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications. 2004; 27(1):133–42. Crossref.
  • Dillon WR, Goldstein M. Multivariate analysis methods and applications. Wiley: New York; 1984.
  • Hand DJ. Discrimination and classification. Wiley: New York; 1981.
  • Aragones JMJ, Ruiz JAG, Jimenez GR, Perez JM, Conejo EA. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artificial Intelligence in Medicine. 2003; 27(1):45–63. Crossref.
  • Ryu YU, Chandrasekaran R, Jacob VS. Breast cancer prediction using the isotonic separation technique. European Journal of Operational Research. 2007; 181(2):842–54.Crossref.
  • Sahan S, Polat K, Kodaz H, Gunes S. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Computers in Biology and Medicine. 2007; 37(3):415–23. Crossref.
  • Krishnamurthy M, Manivannan K, Chilambuchelvan A, Rajalakshmi E, Kannan A. Enhanced Candidate Generation for Frequent Item Set Generation. Indian Journal of Science and Technology. 2015; 8(13):1–7. Crossref.
  • Tynchenko VS, Tynchenko VV, Bukhtoyarov VV, Tynchenko SV, Petrovskyi EA. The multi-objective optimization of complex objects neural network models. Indian Journal of Science and Technology. 2016; 9(29):1–11.
  • Bennett KP, Mangasarian OL. Robust linear programming discrimination of two linearly inseparable sets.Optimization Methods and Software. 1992; 1(1):23–34.Crossref.
  • Bishop CM. Neural networks for pattern recognition. Ist edn, Clarendon Press: Oxford; 2005.
  • Hanbay D, Turkoglu I, Demir Y. An expert system based on wavelet decomposition and neural network for modeling Chua’s circuit. Expert Systems with Applications. 2007; 34(4):2278–83. Crossref.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.