نموذج دعم القرار الذكي لإدارة البيانات في القطاع الصحي بالإعتماد على الحوسبة الضبابية

Intelligent Decision Support Model For Healthcare Data Management Based on Fog Computing

Recently, several healthcare emergencies have occurred globally, which has required and sometimes forced relevant decision-makers to pay overmuch attention to the decision-making process. In the Decision Support System (DSS) architecture, data is considered a backbone component due to DSS bringing together data and knowledge from different areas and sources, processing them, and storing them in the cloud to provide relevant users with meaningful information to support the decision-making process. Hence, DSS deals with cloud data and conventional ways of warehousing huge data in the cloud have a limitation, dealing with such massive volumes of data increases mistakes, packet drop, and the likelihood of data bottlenecks. As a result, it is suitable and essential to use Machine Learning (ML) algorithms in the design and development of an intelligent decision support system in the context of fog-computing. This research proposed an intelligent model for a decision support system in healthcare data management based on fog computing environment (IDSMFOG) using (K-means+ SVM) algorithms, and design instrument for measuring usability of the proposed model named questionnaire usability for DSS (QU-DSS). A DSS based on the proposed model was developed, and later it was also evaluated in terms of usability by QU-DSS instrument. The collected results confirmed that the accuracy of the developed system is (99.87 for USA example, and 99.97 for Iraq example). As well as the results of QU-DSS confirmed that the developed system is usable and support the decision-making process.