[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118348-en":3,"doc-seo-118348-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118348,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Cultivating Expertise - Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning","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.","Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development:  \nRipple Down Rules or Machine Learning  \nAuthor  \nOmar, A , Beydoun , G , Win , K , Shukla , N , Jelinek , HF, Elias , H  \nPublished 2023  \nConference Title ACIS 2023 Proceedings  \nVersion  \nVersion of Record (VoR)  \nRights statement  \n© 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.  \nDownloaded from  \n[https://hdl.handle.net/10072/431427](https://hdl.handle.net/10072/431427)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nAssociation for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ACIS 2023 Proceedings | Australasian (ACIS) |\n| --- | --- |\n| 12-2-2023\u003Cbr>Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning\u003Cbr>Adel Omar\u003Cbr>University of Technology, Sydney, Australia, [adel.omar@student.uts.edu.au](adel.omar@student.uts.edu.au)\u003Cbr>Ghassan Beydoun\u003Cbr>University of Technology, Sydney, Australia, [Ghassan.Beydoun@uts.edu.au](Ghassan.Beydoun@uts.edu.au)\u003Cbr>Khin Than Win\u003Cbr>University of Wollongong, Australia, [win@uow.edu.au](win@uow.edu.au)\u003Cbr>Nagesh Shukla\u003Cbr>Griffith University, QLD Australia, Australia, [n.shukla@griffith.edu.au](n.shukla@griffith.edu.au)\u003Cbr>Herbert Jelinek\u003Cbr>Khalifa University, [herbert.jelinek@ku.ac.ae](herbert.jelinek@ku.ac.ae)\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) |  |\n\nRecommended Citation  \nOmar, 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.  \n[https://aisel.aisnet.org/acis2023/96](https://aisel.aisnet.org/acis2023/96)  \nThis 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).  \nAuthors  \nAdel Omar, Ghassan Beydoun, Khin Than Win, Nagesh Shukla, Herbert Jelinek, and Hector Elias  \nThis article is available at AIS Electronic Library (AISeL): [https://aisel.aisnet.org/acis2023/96](https://aisel.aisnet.org/acis2023/96)  \nCultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning  \nSubmission type: Research-in-progress. Adel Omar  \nSchool of Systems, Management and Leadership University of Technology Sydney  \nSydney, Australia [Email : adel.omar@student.uts.edu.au](Email : adel.omar@student.uts.edu.au)  \nKhin Win  \nSchool of Computing and Information Technology  \nWollongong University Wollongong, Australia [Email : win@uow.edu.au](Email : win@uow.edu.au)  \n[Herbert F. Jelinek](Herbert F. Jelinek)  \nDepartment of Biomedical Engineering Khalifa University of Science and Technology Abu Dhabi, UAE [Email: herbert.jelinek@ku.ac.ae](Email: herbert.jelinek@ku.ac.ae)  \nGhassan Beydoun  \nSchool of Systems, Management & Leadership University of Technology Sydney  \nSydney, Australia [Email : ghassan.beydoun@uts.edu.au](Email : ghassan.beydoun@uts.edu.au)  \nNagesh Shukla  \nDepartment of Business Strategy & Innovation Griffith University  \nGriffith, Australia [Email : n.shukla@griffith.edu.au](Email : n.shukla@griffith.edu.au)  \nHector Elias  \nWollongong Universit","cbCaiq7VhtrGVRBp","https://ap.wps.com/l/cbCaiq7VhtrGVRBp","pdf",581689,1,14,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"What problem does the study address in relation to Type 2 diabetes?\",\"answer\":\"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.\"},{\"question\":\"How does the research compare Ripple Down Rules with machine learning?\",\"answer\":\"It evaluates Ripple Down Rules (RDR) in comparison with machine learning (ML) approaches for the incremental development of a knowledge-based system.\"},{\"question\":\"What performance metrics does the RDR-based system achieve?\",\"answer\":\"The system reports 90.2% accuracy, 96.9% specificity, and 73.6% sensitivity, indicating strong ability to recognize diverse potential cases.\"}]","Cultivating Expertise - 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