Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis

Authors:
Mei-Ling Huang;Yung-Hsiang Hung;Wen-Ming Lee;R. K. Li;Tzu-Hao Wang
Affiliations:
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung, Republic of China 411;Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung, Republic of China 411;Department of Industrial Engineering & Management, National Chiao Tung University, Hsinchu, Republic of China 300;Department of Industrial Engineering & Management, National Chiao Tung University, Hsinchu, Republic of China 300;Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung, Republic of China 411
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.