Title :
Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses
Author :
Ashraf, M. ; Le, Kim ; Huang, Xu
Author_Institution :
ISE, Univ. of Canberra, Bruce, ACT, Australia
fDate :
Nov. 30 2010-Dec. 2 2010
Abstract :
This paper presents a new approach for breast cancer diagnosis using a combination of an Adaptive Network based Fuzzy Inference System (ANFIS) and the Information Gain method. In this approach, the ANFIS is to build an input-output mapping using both human knowledge and machine learning ability and the information gain method is to reduce the number of input features to ANFIS. An experimental result shows 98.23% accuracy which underlines the capability of the proposed algorithm.
Keywords :
cancer; fuzzy reasoning; learning (artificial intelligence); medical diagnostic computing; neural nets; adaptive neurofuzzy inference system; breast cancer diagnosis; human knowledge; information gain method; input-output mapping; machine learning ability; Accuracy; Adaptive systems; Artificial neural networks; Breast cancer; Humans; Machine learning; Adaptive Neuro-Fuzzy Inference Systems; Breast cancer diagnoses; Information Gain; Sugeno Inference System;
Conference_Titel :
Computer Sciences and Convergence Information Technology (ICCIT), 2010 5th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-8567-3
Electronic_ISBN :
978-89-88678-30-5
DOI :
10.1109/ICCIT.2010.5711189
