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.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Huang ML,Hung YH,Lee WM,Li RK,Wang THdoi
10.1007/s10916-010-9485-0subject
Has Abstractpub_date
2012-04-01 00:00:00pages
407-14issue
2eissn
0148-5598issn
1573-689Xjournal_volume
36pub_type
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