Journals Information
Universal Journal of Public Health Vol. 10(3), pp. 241 - 250
DOI: 10.13189/ujph.2022.100301
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A Self-Learning Knowledge Based System to Mitigate the Impact of Drug Interactions in Type Two Diabetes Mellitus
Amansisa Embabo 1, Worku Jimma 2,*, Chala Diriba 2
1 Department of Information Technology, Faculty of Engineering and Technology, Mettu University, Ethiopia
2 Department of Information Science, Faculty of Computing and Informatics, Institute of Technology, Jimma University, Ethiopia
ABSTRACT
The topic of drug interactions has received great attention worldwide recently. If a drug is not administered in appropriate quantity with an appropriate combination of the drug with drug or other substances, the result will be a high risk of dangerous interactions which lead to potentially harmful side effects which are ranging from treatment failure, economic degradation, and death due to lack of drug information and maladministration of drugs among health professionals. Thus, this study was initiated with the main aim to develop a self-learning knowledge-based system to mitigate the impact of drug interactions. To develop this system, design science methodology with the integration of knowledge engineering method was used; and semi-structured interview, document analysis to acquire knowledge; questionnaire to grasp users' feedback, and purposive sampling technique to select domain experts were used. The acquired knowledge was then represented using a production rule and modelled using a decision tree. The system was implemented using PROLOG on SWI-PROLOG editor and evaluated using system performance and user acceptance testing. The developed system was evaluated and 96% of the users were satisfied. As well as the performance of the system was evaluated and recorded 80% accuracy, thus it can be concluded that the system achieves good performance. However, in order to make the system fully applicable in the domain area, further research work to incorporate adequate knowledge, develop online knowledge-based systems and mobile applications were recommended to enhance the accessibility of the developed system.
KEYWORDS
Diabetes Mellitus, Drug Interactions, Knowledge-Based System, Self-Learning, Model
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Amansisa Embabo , Worku Jimma , Chala Diriba , "A Self-Learning Knowledge Based System to Mitigate the Impact of Drug Interactions in Type Two Diabetes Mellitus," Universal Journal of Public Health, Vol. 10, No. 3, pp. 241 - 250, 2022. DOI: 10.13189/ujph.2022.100301.
(b). APA Format:
Amansisa Embabo , Worku Jimma , Chala Diriba (2022). A Self-Learning Knowledge Based System to Mitigate the Impact of Drug Interactions in Type Two Diabetes Mellitus. Universal Journal of Public Health, 10(3), 241 - 250. DOI: 10.13189/ujph.2022.100301.