COMPARATIVE STUDY ON DIFFERENT CLASSIFICATION TECHNIQUES FOR BREAST CANCER DATASET

Breast cancer is one of the most common cancers among women in the world. Early detection of breast cancer is essential in reducing their life losses. Data mining is the process of analyzing massive data and summarizing it into useful knowledge discovery and the role of data mining approaches is growing rapidly especially classification techniques are very effective way to classifying the data, which is essential in decision-making process for medical practitioners. This study presents the different data mining classifiers on the database of breast cancer, by using classification accuracy with and without feature selection techniques. Feature selection increases the accuracy of the classifier because it eliminates irrelevant attributes. The experiment shows that the feature selection enhances the accuracy of all three different classifiers, reduces the Mean Standard Error (MSE) and increase Receiver Operating Characteristics (ROC).

[1]  E. Ramaraj,et al.  Classification algorithm in Data mining : An Overview , 2013 .

[2]  K. Usha Rani,et al.  ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA , 2012 .

[3]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[4]  Philip H. Goodman,et al.  Comparing artificial neural networks to other statistical methods for medical outcome prediction , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[5]  Hiroshi Tanaka,et al.  Comparison of Seven Algorithms to Predict Breast Cancer Survival( Contribution to 21 Century Intelligent Technologies and Bioinformatics) , 2008 .

[6]  R. Chang,et al.  Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images , 2001, Breast Cancer Research and Treatment.

[7]  P. S. Pawar,et al.  Breast Cancer Detection Using Neural Network Models , 2013, 2013 International Conference on Communication Systems and Network Technologies.

[8]  T. Sridevi,et al.  Rough Set Theory Based Attribute Reduction for Breast Cancer Diagnosis , 2012 .

[9]  A. Vlahou,et al.  Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data , 2003, Journal of biomedicine & biotechnology.

[10]  L. V. Nandakishore,et al.  AN EMPIRICAL COMPARISON OF SUPERVISED LEARNING ALGORITHMS IN DISEASE DETECTION , 2011 .

[11]  Wai Lok Woo,et al.  Breast cancer prediction and cross validation using multilayer perceptron neural networks , 2010, 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010).

[12]  Xin Yao,et al.  Neural networks for breast cancer diagnosis , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  K. Usha Rani,et al.  A Hybrid Approach to Improve Classification with Cascading of Data Mining Tasks , 2013 .

[14]  W. Vach,et al.  On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. , 2000, Statistics in medicine.

[15]  K. Duraiswamy,et al.  Evaluation of three neural network models using Wisconsin breast cancer database , 2009, 2009 International Conference on Control, Automation, Communication and Energy Conservation.

[16]  Euripidis N. Loukis,et al.  Using decision tree algorithms as a basis for a heart sound diagnosis decision support system , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..