Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach

Tianhua Chen; Changjing Shang; Pan  Su; Grigoris Antoniou; Qiang Shen

doi:10.1007/978-3-319-97982-3_19

Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach

Tianhua Chen, Changjing Shang, Pan Su, Grigoris Antoniou, Qiang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 18th UK Workshop on Computational Intelligence
Editors	Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
Publisher	Springer, Cham
Pages	227-239
Number of pages	13
ISBN (Electronic)	9783319979823
ISBN (Print)	9783319979816
DOIs	https://doi.org/10.1007/978-3-319-97982-3_19
Publication status	Published - 12 Aug 2018
Event	18th UK Workshop on Computational Intelligence - Nottingham Trent University, Nottingham, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018 Conference number: 18 http://ukci2018.uk/ (Link to Workshop Website)

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Workshop

Workshop	18th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2018
Country/Territory	United Kingdom
City	Nottingham
Period	5/09/18 → 7/09/18
Internet address	http://ukci2018.uk/ (Link to Workshop Website)

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-319-97982-3_19Licence: Unspecified

UKCI-2018_paper_13Accepted author manuscript, 652 KBLicence: Unspecified

Cite this

Chen, T., Shang, C., Su, P., Antoniou, G., & Shen, Q. (2018). Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach. In A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, & M. McGinnity (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence (pp. 227-239). (Advances in Intelligent Systems and Computing). Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_19

Chen, Tianhua ; Shang, Changjing ; Su, Pan et al. / Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach. Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence. editor / Ahmad Lotfi ; Hamid Bouchachia ; Alexander Gegov ; Caroline Langensiepen ; Martin McGinnity. Springer, Cham, 2018. pp. 227-239 (Advances in Intelligent Systems and Computing).

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title = "Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach",

abstract = "Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.",

keywords = "Crisp Rules, Fuzzy Rules, Fuzzy Rule Base, Pima Indians Diabetes Dataset, Decision Tree Learning",

author = "Tianhua Chen and Changjing Shang and Pan Su and Grigoris Antoniou and Qiang Shen",

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booktitle = "Advances in Computational Intelligence Systems",

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Chen, T, Shang, C, Su, P, Antoniou, G & Shen, Q 2018, Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach. in A Lotfi, H Bouchachia, A Gegov, C Langensiepen & M McGinnity (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence. Advances in Intelligent Systems and Computing, Springer, Cham, pp. 227-239, 18th UK Workshop on Computational Intelligence, Nottingham, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-97982-3_19

Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach. / Chen, Tianhua; Shang, Changjing; Su, Pan et al.
Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence. ed. / Ahmad Lotfi; Hamid Bouchachia; Alexander Gegov; Caroline Langensiepen; Martin McGinnity. Springer, Cham, 2018. p. 227-239 (Advances in Intelligent Systems and Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach

AU - Chen, Tianhua

AU - Shang, Changjing

AU - Su, Pan

AU - Antoniou, Grigoris

AU - Shen, Qiang

N1 - Conference code: 18

PY - 2018/8/12

Y1 - 2018/8/12

N2 - Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.

AB - Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.

KW - Crisp Rules

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KW - Fuzzy Rule Base

KW - Pima Indians Diabetes Dataset

KW - Decision Tree Learning

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M3 - Conference contribution

SN - 9783319979816

T3 - Advances in Intelligent Systems and Computing

SP - 227

EP - 239

BT - Advances in Computational Intelligence Systems

A2 - Lotfi, Ahmad

A2 - Bouchachia, Hamid

A2 - Gegov, Alexander

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PB - Springer, Cham

T2 - 18th UK Workshop on Computational Intelligence

Y2 - 5 September 2018 through 7 September 2018

ER -

Chen T, Shang C, Su P, Antoniou G, Shen Q. Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach. In Lotfi A, Bouchachia H, Gegov A, Langensiepen C, McGinnity M, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence. Springer, Cham. 2018. p. 227-239. (Advances in Intelligent Systems and Computing). Epub 2018 Aug 11. doi: 10.1007/978-3-319-97982-3_19

Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach

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