Cultivating Expertise - Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning

Aug 2, 2026

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Type 2 diabetes is a chronic condition driven by intertwined genetic and environmental factors, with social determinants exerting a substantial influence. Given the rising prevalence, the study evaluates incremental development of a knowledge-based system to support effective T2D management by modeling the evolving impact of social determinants. Ripple Down Rules are compared against machine learning approaches to build a robust decision support system. Results confirm the approach’s viability: the RDR-enhanced system attains 90.2% accuracy, 96.9% specificity, and 73.6% sensitivity for identifying potential cases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

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1 Introduction

Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning Author Omar, A , Beydoun , G , Win , K , Shukla , N , Jelinek , HF, Elias , H Published 2023 Conference Title ACIS 2023 Proceedings Version Version of Record (VoR) Rights statement © 2023 Omar, Beydoun , Win , Shukla , Jelinek and Elias. This is an open-access article licensed under a Creative Commons Attribution-Non-Commercial 4.0 Australia License , which permits non-commercial use , distribution , and reproduction in any medium , provided the original author and ACIS are credited. Downloaded from [https://hdl.handle.net/10072/431427](https://hdl.handle.net/10072/431427) Griffith Research Online [https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au) Association for Information Systems AIS Electronic Library (AISeL) | ACIS 2023 Proceedings | Australasian (ACIS) | | --- | --- | | 12-2-2023
Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning
Adel Omar
University of Technology, Sydney, Australia, [adel.omar@student.uts.edu.au](adel.omar@student.uts.edu.au)
Ghassan Beydoun
University of Technology, Sydney, Australia, [Ghassan.Beydoun@uts.edu.au](Ghassan.Beydoun@uts.edu.au)
Khin Than Win
University of Wollongong, Australia, [win@uow.edu.au](win@uow.edu.au)
Nagesh Shukla
Griffith University, QLD Australia, Australia, [n.shukla@griffith.edu.au](n.shukla@griffith.edu.au)
Herbert Jelinek
Khalifa University, [herbert.jelinek@ku.ac.ae](herbert.jelinek@ku.ac.ae)
See next page for additional authors
Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) | | Recommended Citation Omar, Adel; Beydoun, Ghassan; Win, Khin Than; Shukla, Nagesh; Jelinek, Herbert; and Elias, Hector,"Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning" (2023) . ACIS 2023 Proceedings. 96. [https://aisel.aisnet.org/acis2023/96](https://aisel.aisnet.org/acis2023/96) This material is brought to you by the Australasian (ACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ACIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org). Authors Adel Omar, Ghassan Beydoun, Khin Than Win, Nagesh Shukla, Herbert Jelinek, and Hector Elias This article is available at AIS Electronic Library (AISeL): [https://aisel.aisnet.org/acis2023/96](https://aisel.aisnet.org/acis2023/96) Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning Submission type: Research-in-progress. Adel Omar School of Systems, Management and Leadership University of Technology Sydney Sydney, Australia [Email : adel.omar@student.uts.edu.au](Email : adel.omar@student.uts.edu.au) Khin Win School of Computing and Information Technology Wollongong University Wollongong, Australia [Email : win@uow.edu.au](Email : win@uow.edu.au) [Herbert F. Jelinek](Herbert F. Jelinek) Department of Biomedical Engineering Khalifa University of Science and Technology Abu Dhabi, UAE [Email: herbert.jelinek@ku.ac.ae](Email: herbert.jelinek@ku.ac.ae) Ghassan Beydoun School of Systems, Management & Leadership University of Technology Sydney Sydney, Australia [Email : ghassan.beydoun@uts.edu.au](Email : ghassan.beydoun@uts.edu.au) Nagesh Shukla Department of Business Strategy & Innovation Griffith University Griffith, Australia [Email : n.shukla@griffith.edu.au](Email : n.shukla@griffith.edu.au) Hector Elias Wollongong Universit

FAQs

What problem does the study address in relation to Type 2 diabetes?

The study addresses the need for inventive management strategies for Type 2 diabetes by focusing on how evolving social determinants influence outcomes and decision-making.

How does the research compare Ripple Down Rules with machine learning?

It evaluates Ripple Down Rules (RDR) in comparison with machine learning (ML) approaches for the incremental development of a knowledge-based system.

What performance metrics does the RDR-based system achieve?

The system reports 90.2% accuracy, 96.9% specificity, and 73.6% sensitivity, indicating strong ability to recognize diverse potential cases.

Aug 2, 2026

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