Maximal product-based intuitionistic fuzzy line graphs for healthcare predictive analysis

This paper explores the applications of Intuitionistic Fuzzy Graphs ( IFG ) representing uncertainty and imprecision in complex systems through the analysis of correlation and regression coefficients ( CRCs ) with focus on the maximal product. The study examines the relationships between the edges of the graph by analysing the line graph derived from IFG , facilitating a deeper understanding of the network’s dynamics. The construction of adjacency matrices that incorporate both membership and non-membership values enables the calculation of energy and weight scores, quantifying the strength and predictive correlations among variables. Furthermore, the study discusses the complement of Intuitionistic Fuzzy Line Graphs ( IFLG ) , using maximal product analysis to uncover concealed relationships within the network. MATLAB is used to generate heatmaps that visually represent the importance of correlation to critical network characteristics. The practical importance is demonstrated in a healthcare context, particularly in predicting diabetes risk by modelling factors of glucose levels, body mass index (BMI), and insulin. Heatmaps can be effectively visualized to show interrelationships between these features, aiding in the interpretation of network patterns.