The Incremental Development of a Type 2 Diabetes Knowledge-Based System using Ripple-Down Rules - A Socio-Technical Perspective on Managing Type 2 Diabetes

Omar, Adel

The Incremental Development of a Type 2 Diabetes Knowledge-Based System using Ripple-Down Rules - A Socio-Technical Perspective on Managing Type 2 Diabetes

Omar, Adel

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Omar, Adel
dc.date.accessioned	2026-04-08T04:06:08Z
dc.date.available	2026-04-08T04:06:08Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/194604
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This thesis develops an incremental knowledge-based system (KBS) for type 2 diabetes that integrates social determinants of health using Ripple-Down Rules (RDR). Motivated by scarce, heterogeneous data, the approach captures expert refinements as exception rules, enabling transparent updates without large training sets. Compared with conventional machine-learning pipelines that may discard sparse cases as outliers, the RDR-based KBS accommodates contextual factors while remaining auditable and interpretable. The method iteratively integrates new evidence on social determinants and standardises area-level attributes, producing a rule base that adapts to emerging knowledge and variable data quality. The framework is scalable to other settings and supports policy design by revealing stable associations between socio-demographic factors and risk. Empirical evaluation confirms the practicality and durability of RDRs for building and maintaining a diabetes KBS under data paucity. Overall, the work provides a reproducible pathway for knowledge-driven decision support in public health informatics, demonstrating how social determinants can be operationalised in an interpretable system for disease prevention and management.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/194604/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2025 Adel Omar
dc.rights	au.edu.uts.lib/cph
dc.title	The Incremental Development of a Type 2 Diabetes Knowledge-Based System using Ripple-Down Rules - A Socio-Technical Perspective on Managing Type 2 Diabetes	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis develops an incremental knowledge-based system (KBS) for type 2 diabetes that integrates social determinants of health using Ripple-Down Rules (RDR). Motivated by scarce, heterogeneous data, the approach captures expert refinements as exception rules, enabling transparent updates without large training sets. Compared with conventional machine-learning pipelines that may discard sparse cases as outliers, the RDR-based KBS accommodates contextual factors while remaining auditable and interpretable. The method iteratively integrates new evidence on social determinants and standardises area-level attributes, producing a rule base that adapts to emerging knowledge and variable data quality. The framework is scalable to other settings and supports policy design by revealing stable associations between socio-demographic factors and risk. Empirical evaluation confirms the practicality and durability of RDRs for building and maintaining a diabetes KBS under data paucity. Overall, the work provides a reproducible pathway for knowledge-driven decision support in public health informatics, demonstrating how social determinants can be operationalised in an interpretable system for disease prevention and management.

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http://hdl.handle.net/10453/194604