Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis

Mei Ling Huang*, Yung Hsiang Hung, Wen Ming Lee, Rong-Kwei Li, Tzu Hao Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

44 Scopus citations

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.

Original languageEnglish
Pages (from-to)407-414
Number of pages8
JournalJournal of Medical Systems
Volume36
Issue number2
DOIs
StatePublished - 1 Apr 2012

Keywords

  • ANFIS
  • Breast cancer
  • Case-based reasoning
  • Particle swarm optimizer

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