Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis.
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.
Huang
Mei-Ling
ML
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taiping, Taiwan, Republic of China. huangml@ncut.edu.tw
Hung
Yung-Hsiang
YH
Lee
Wen-Ming
WM
Li
R K
RK
Wang
Tzu-Hao
TH
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis*; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D055815:Young Adult