An Expert System for Detection of Breast Cancer Using Data Preprocessing and Bayesian Network

This paper presents an automatic system for detection of breast cancer using data preprocessing and Bayesian network. In this study, ReliefF algorithm is used for reducing the dimension of breast cancer database then a pre-processing is done on the data and ultimately Bayesian network classifier is used for classification. The system performance has been compared with model NN (neural network) and AR + NN (neural networks combined with association rules). The dimension of input feature space is reduced from nine to eight by using ReliefF. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performance. The correct classification rate of proposed system is 98.1%.This research offered that the preprocessing is necessary on this data and combination of ReliefF and Bayesian network can be used to obtain fast automatic diagnostic systems for breast cancer.

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  R. Bouckaert.  Bayesian belief networks : from construction to inference , 1995 .

[3]  José Antonio Gómez-Ruiz,et al.  A combined neural network and decision trees model for prognosis of breast cancer relapse , 2003, Artif. Intell. Medicine.

[4]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[5]  Concha Bielza,et al.  Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process , 2009, Expert Syst. Appl..

[6]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[7]  Elif Derya íbeyli.  Implementing automated diagnostic systems for breast cancer detection , 2007 .

[8]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[9]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[10]  Marko Robnik-Sikonja,et al.  An adaptation of Relief for attribute estimation in regression , 1997, ICML.

[11]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[12]  Yetian Chen,et al.  Learning Classifiers from Imbalanced, Only Positive and Unlabeled Data Sets , 2008 .

[13]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[14]  M. Cevdet Ince,et al.  An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..