Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Muhammad Ashraf; Kim Le; Xu Huang

doi:10.1109/ICCIT.2010.5711189

Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Muhammad Ashraf
, Kim Le
, Xu Huang

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

13 Citations (Scopus)

129 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings: 5th International Conference on Computer Sciences and Convergence Information Technology: ICCIT 2010
Place of Publication	Piscataway, N.J., USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	911-915
Number of pages	5
Volume	2
ISBN (Print)	9788988678305
DOIs	https://doi.org/10.1109/ICCIT.2010.5711189
Publication status	Published - 2010
Event	5th ICCIT: 2010 International Conference on Computer Sciences and Convergence Information Technology - Seoul, Korea, Republic of Duration: 30 Nov 2010 → 2 Dec 2010

Conference

Conference	5th ICCIT: 2010 International Conference on Computer Sciences and Convergence Information Technology
Country/Territory	Korea, Republic of
City	Seoul
Period	30/11/10 → 2/12/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1109/ICCIT.2010.5711189

PaperFinal published version, 605 KBLicence: Other

Cite this

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title = "Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses",

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. ",

author = "Muhammad Ashraf and Kim Le and Xu Huang",

year = "2010",

doi = "10.1109/ICCIT.2010.5711189",

language = "English",

isbn = "9788988678305",

volume = "2",

pages = "911--915",

booktitle = "Proceedings: 5th International Conference on Computer Sciences and Convergence Information Technology: ICCIT 2010",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "5th ICCIT: 2010 International Conference on Computer Sciences and Convergence Information Technology ; Conference date: 30-11-2010 Through 02-12-2010",

}

Ashraf, M, Le, K & Huang, X 2010, Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses. in Proceedings: 5th International Conference on Computer Sciences and Convergence Information Technology: ICCIT 2010. vol. 2, IEEE, Institute of Electrical and Electronics Engineers, Piscataway, N.J., USA, pp. 911-915, 5th ICCIT: 2010 International Conference on Computer Sciences and Convergence Information Technology, Seoul, Korea, Republic of, 30/11/10. https://doi.org/10.1109/ICCIT.2010.5711189

Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses. / Ashraf, Muhammad; Le, Kim; Huang, Xu.
Proceedings: 5th International Conference on Computer Sciences and Convergence Information Technology: ICCIT 2010. Vol. 2 Piscataway, N.J., USA: IEEE, Institute of Electrical and Electronics Engineers, 2010. p. 911-915.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

AU - Ashraf, Muhammad

AU - Le, Kim

AU - Huang, Xu

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1109/ICCIT.2010.5711189

DO - 10.1109/ICCIT.2010.5711189

M3 - Conference contribution

SN - 9788988678305

VL - 2

SP - 911

EP - 915

BT - Proceedings: 5th International Conference on Computer Sciences and Convergence Information Technology: ICCIT 2010

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway, N.J., USA

T2 - 5th ICCIT: 2010 International Conference on Computer Sciences and Convergence Information Technology

Y2 - 30 November 2010 through 2 December 2010

ER -

Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Abstract

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UN SDGs

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