A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques

Wenlin Zhang; Syed Ghufran khalid; Soban Sadiq; Haipeng Liu; Janet Yuen Ha Wong

doi:10.1016/B978-0-323-95686-4.00001-0

A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques

Wenlin Zhang
, Syed Ghufran khalid
, Soban Sadiq
, Haipeng Liu
, Janet Yuen Ha Wong

Zhejiang University
Nottingham Trent University
Kent and Medway Medical School
Hong Kong Metropolitan University

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

2 Citations (Scopus)

Abstract

To improve the diagnostic reliability of diabetes mellitus (DM), rule-based machine learning techniques have been proposed. However, the existing studies are highly diverse with a lack of summarization on the state-of-the-art. To address this gap, we comprehensively reviewed some recent studies. Overall, rule-based methods improved the performance and explainability of the machine learning algorithms, providing direct reference for personalized recommendation and clinical intervention of DM. However, the quality and availability of data limited the reliability of the algorithms. The current algorithms focus on fuzzy system and its optimizations, with a scarce of more complex methods. In the future, the rule-based machine learning algorithms can be improved by using large-scale datasets and more complex structures with better clinical knowledge interpretation, where Internet-of-things and advanced artificial intelligence algorithms will play a key role.

Original language	English
Title of host publication	Internet of Things and Machine Learning for Type I and Type II Diabetes
Subtitle of host publication	Use cases
Editors	Sujata Dash, Subhendu Kumar Pani, Willy Susilo, Bernard Man Yung Cheung, Gary Tse
Publisher	Elsevier
Chapter	1
Pages	3-16
Number of pages	14
Edition	1
ISBN (Print)	978-0-323-95686-4
DOIs	https://doi.org/10.1016/B978-0-323-95686-4.00001-0
Publication status	Published - 19 Jul 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

AI-assisted diagnosis
Artificial intelligence
Diabetes
Fuzzy logic
Fuzzy system
Healthcare data analytics
Rule-based machine learning
Systematic review

Access to Document

10.1016/B978-0-323-95686-4.00001-0

Cite this

Zhang, W., khalid, S. G., Sadiq, S., Liu, H., & Wong, J. Y. H. (2024). A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques. In S. Dash, S. K. Pani, W. Susilo, B. M. Y. Cheung, & G. Tse (Eds.), Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases (1 ed., pp. 3-16). Elsevier. https://doi.org/10.1016/B978-0-323-95686-4.00001-0

A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques. / Zhang, Wenlin; khalid, Syed Ghufran; Sadiq, Soban et al.
Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. ed. / Sujata Dash; Subhendu Kumar Pani; Willy Susilo; Bernard Man Yung Cheung; Gary Tse. 1. ed. Elsevier, 2024. p. 3-16.

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

Zhang, W, khalid, SG, Sadiq, S, Liu, H & Wong, JYH 2024, A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques. in S Dash, SK Pani, W Susilo, BMY Cheung & G Tse (eds), Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. 1 edn, Elsevier, pp. 3-16. https://doi.org/10.1016/B978-0-323-95686-4.00001-0

Zhang, Wenlin ; khalid, Syed Ghufran ; Sadiq, Soban et al. / A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques. Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. editor / Sujata Dash ; Subhendu Kumar Pani ; Willy Susilo ; Bernard Man Yung Cheung ; Gary Tse. 1. ed. Elsevier, 2024. pp. 3-16

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A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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