Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

An Information Gain based Fuzzy Classifier for Predictive Analysis in Colon Cancer Data

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011

N.S.Nithya and Dr.K.Duraiswamy. Article:An Information Gain based Fuzzy Classifier for Predictive Analysis in Colon Cancer Data. International Journal of Computer Applications 31(6):45-48, October 2011. Full text available. BibTeX

	author = {N.S.Nithya and Dr.K.Duraiswamy},
	title = {Article:An Information Gain based Fuzzy Classifier for Predictive Analysis in Colon Cancer Data},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {31},
	number = {6},
	pages = {45-48},
	month = {October},
	note = {Full text available}


Modern medicine generates a great deal of information stored in the medical database. Extracting useful knowledge and providing scientific decision making for the diagnosis and treatment of disease from the database increasingly becomes necessary. In India most of the people suffering cancer diseases. Using association rule mining for constructing classification system for diagnosing cancer diseases is a promising approach. A detailed survey shows that a combined approach which integrates the Fuzzy weighted association mining and information gain method may be used to find the associated attribute based on information gain which assigns a weight value to support ,confidence measure and also a fuzzy association mining rule may be used to classify the cancer diseases. This approach would provide a better accuracy compared to other association rule mining technique.


  • Jyoti Soni,Ujma Ansari, Dipesh Sharma and Sunita Soni. 2011. Predictive Data Mining for Medical Diagnosis:An Overview of Heart Disease Prediction, J. Comp. Appl.17(8).
  • Ephziabah, E.P. 2011. Cost Effective Approch on Feature Selection Using Genetic Algorithms and Fuzzy Logic For Diabetes Diagnosis, J. Soft Comp. 2(1), pp. 1-10.
  • Vikram Pudi and Radha Krishna, P. 2009. Data Mining, Oxford University Press.
  • Ashraf, M., Kim Le and Xu Huang. Information Gain and Adaptive Neuro-Fuzzy Inference System for Breast Cancer Diagnoses,pp.911-915.
  • Parvinder, S., Sandhu, Dalvinder,S., Dhaliwai and Panda, S.N. 2011. Mining Utility-Oriented Association Rules: An Efficient Approach Based on Profit and Quantity, J.Phy. Scie., 6(2), pp. 301-307.
  • Chien-Hua Wang and Chin-Tzong Pang.2009.Finding Fuzzy Association Rule Using FWFP-Growth with Linguistic Supports and Confidences, J. Info.and Mathe. Sci., 5(4), pp. 300-308.
  • Murtagh, Tao.F., Farid, F. 2003.M:Weighted Association Rule Mining Using Weighted support and siginificance Framework, Proceedings of 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,pp.661-666.
  • Ruijuan Hu. 2010. Medical Data Mining Based on Association Rules, J.Comp. and Info. Sci., 3(4).
  • Sunitha Soni, Vyas, O.P. 2010. Using Associative Classifiers for Predictive Analysis in Health Care Data Mining, J. Comp. Appl.,4(5), pp.33-37.
  • Saket Agarwal and Leena Singh.2011.Mining Fuzzy Association Rule Using Fuzzy Artmap for Clustering, Journal of Engineering Research and Studies, 2, pp.76-80.
  • Vijay Krishna,V and Radha Krishna, P. 2008. A novel approach for statistical and fuzzy association rule mining on quantitative data, J. Sci. & Indu. Research, 67,pp. 512-517.
  • Essam AI-Daoud.2010.Cancer Diagnosis Using Modified Fuzzy Network, Universal J. Comp. Sci. and Engg. Tech.,1(2),pp. 73-78.
  • Maybin Muyeba, M.Sulaiman Khan and Frans Coenen. 2010. Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework, Scientific literature Digital Library and Search engine.