| Title: | Maximal product-based intuitionistic fuzzy line graphs for healthcare predictive analysis |
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| Authors: | ID Meenakshi, Annamalai (Author)
ID Mishra, J. Shivangi (Author)
ID Mršić, Leo (Author)
ID Kalampakas, Antonios (Author)
ID Samanta, Sovan (Author)
ID Allahviranloo, Tofigh (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S209044792500680X?via%3Dihub
 PDF - Presentation file, download (3,20 MB)
MD5: FD39C53964433C1E411AFCE6EC2C230F
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  RUDOLFOVO - Rudolfovo - Science and Technology Centre Novo Mesto
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| Abstract: | This paper explores the applications of Intuitionistic Fuzzy Graphs (ℐ ℱ � ) representing uncertainty and impre cision in complex systems through the analysis of correlation and regression coefficients (� ℛ� �) with focus on the maximal product. The study examines the relationships between the edges of the graph by analysing the line graph derived from ℐ ℱ � , 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 (ℐ ℱ ℒ � ), using maximal product anal ysis 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 demon strated 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. |
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| Keywords: | intuitionistic fuzzy graphs, intuitionistic fuzzy line graphs, maximal product, adjacency matrices, correlation and regression coefficients |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 07.01.2026 |
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| Publisher: | Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-12 |
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| Numbering: | Vol. 17, iss. 1, art. 103939 |
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| PID: | 20.500.12556/DiRROS-27390  |
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| UDC: | 519.17 |
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| ISSN on article: | 2090-4495 |
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| DOI: | 10.1016/j.asej.2025.103939 |
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| COBISS.SI-ID: | 266357251 |
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| Copyright: | © 2025 The Author(s). |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 27. 1. 2026;
Soavtorji: J. Shivangi Mishra, Leo Mršić, Antonios Kalampakas, Sovan Samanta, Tofigh Allahviranloo;
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| Publication date in DiRROS: | 04.02.2026 |
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| Views: | 44 |
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| Downloads: | 18 |
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