A Stable Intelligent Model-Based Framework Using Graph Convolutional Networks and MULTIMOORA for Clinical Decision Support Systems

Artificial intelligence (AI) is rapidly advancing in consumer electronics, particularly in clinical decision support systems (CDSSs). AI-driven devices enhance the effectiveness of healthcare-related electronics via predictive health analytics, reshaping medical data collection, analysis, and decision-making. Nevertheless, due to the abundance of imprecise, hesitant, and fuzzy information in healthcare data stemming from consumer electronics, AI may misinterpret the data, resulting in erroneous and unstable medical decisions. Therefore, this paper endeavors to establish a stable intelligent medical decision model, employing two essential tools: graph convolutional networks (GCNs) and MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form). First, a granular computing (GrC) framework based on GCNs and MULTIMOORA is established. In this framework, GCNs are initially employed to process multi-modal data within the hesitant fuzzy linguistic (HFL) background. Second, a comprehensive HFL information system (IS) is constructed. Third, three types of multi-granularity (MG) HFL methods are developed via MULTIMOORA. To better address the bounded rationality of decision-makers (DMs), regret theory (RT) is utilized to consolidate the multi-modal data. Finally, the efficacy and practically of the proposed decision model are assessed via a case study on medication selections.