The Incremental Development of a Diabetes 2 Knowledge Base System using Ripple Down Rules

Authors

Adel Omar, University of Technology SydneyFollow
Ghassan Beydoun, University of Technology SydneyFollow
Khin Than Win, University of WollongongFollow
Herbert Jelinek, Khalifa UniversityFollow

Paper Number

1500

Abstract

It has been proposed that social determinants play a role in identifying the type of person likely to develop Type 2 diabetes. Social attributes such as highest education level achieved, family history or place of residence and/or birth, all play a strong role in the probability of a person developing Type 2 diabetes later in life. Social determinants should therefore be able to be used to build a knowledge base (KB) as a part of a decision support system (DSS) for Type 2 diabetes prediction. The results of the current research are promising. The decision support system (DSS) created using Ripple Down Rules (RDRs) achieved an accuracy of 84.5%, a specificity of 92.3% and a sensitivity of 46% using 79 RDRs.

Comments

Paper Number 1500

Recommended Citation

Omar, Adel; Beydoun, Ghassan; Win, Khin Than; and Jelinek, Herbert, "The Incremental Development of a Diabetes 2 Knowledge Base System using Ripple Down Rules" (2022). PACIS 2022 Proceedings. 18.
https://aisel.aisnet.org/pacis2022/18