標題: Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis
作者: Huang, Mei-Ling
Hung, Yung-Hsiang
Lee, Wen-Ming
Li, R. K.
Wang, Tzu-Hao
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Case-based reasoning;Particle swarm optimizer;ANFIS;Breast cancer
公開日期: 1-四月-2012
摘要: Breast cancer is a common to females world-wide. 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.
URI: http://hdl.handle.net/11536/16050
ISSN: 0148-5598
期刊: JOURNAL OF MEDICAL SYSTEMS
Volume: 36
Issue: 2
結束頁: 407
顯示於類別:期刊論文


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