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 proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 18th UK Workshop on Computational Intelligence
EditorsAhmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
PublisherSpringer, Cham
Pages227-239
Number of pages13
ISBN (Electronic)9783319979823
ISBN (Print)9783319979816
DOIs
Publication statusPublished - 12 Aug 2018
Event18th Annual UK Workshop on Computational Intelligence - Nottingham Trent University, Nottingham, United Kingdom
Duration: 5 Sep 20187 Sep 2018
Conference number: 18
http://ukci2018.uk/ (Link to Workshop Website)

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Workshop

Workshop18th Annual UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2018
CountryUnited Kingdom
CityNottingham
Period5/09/187/09/18
Internet address

Fingerprint

Medical problems
Decision trees
Fuzzy rules
Hazards
Health

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 ; Antoniou, Grigoris ; Shen, Qiang. / 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).
@inproceedings{819c6b787491457d9f4cf1a05963a452,
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.",
author = "Tianhua Chen and Changjing Shang and Pan Su and Grigoris Antoniou and Qiang Shen",
year = "2018",
month = "8",
day = "12",
doi = "10.1007/978-3-319-97982-3_19",
language = "English",
isbn = "9783319979816",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer, Cham",
pages = "227--239",
editor = "Ahmad Lotfi and Hamid Bouchachia and Alexander Gegov and Caroline Langensiepen and Martin McGinnity",
booktitle = "Advances in Computational Intelligence Systems",

}

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 Annual 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 ; Antoniou, Grigoris; Shen, Qiang.

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 proceedingConference contribution

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

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.

UR - http://www.scopus.com/inward/record.url?scp=85052189851&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-97982-3_19

DO - 10.1007/978-3-319-97982-3_19

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

A2 - Langensiepen, Caroline

A2 - McGinnity, Martin

PB - Springer, Cham

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). https://doi.org/10.1007/978-3-319-97982-3_19