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Article type: Research Article
Authors: Alqaissi, Emana; b; * | Alotaibi, Fahda | Ramzan, Muhammad Shera | Algarni, Abdulmohsenc
Affiliations: [a] Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia | [b] Department of Information Systems, The Applied College, King Khalid University, Abha, Saudi Arabia | [c] Department of Computer Science, King Khalid University, Abha, Saudi Arabia
Correspondence: [*] Corresponding author. Eman Alqaissi, E-mail: [email protected].
Abstract: The influenza virus can spread easily, causing significant public health concern. Despite the existence of different techniques for rapid detection and prevention of influenza, their efficiency varies significantly. Additionally, there is currently a lack of a comprehensive, interoperable, and reusable real-time model for detecting influenza infection and predicting relationships within the field of influenza analysis. This study proposed a comprehensive, real-time model for rapid and early influenza detection using symptoms. Further, new relationships in the influenza field were discovered. Multiple data sources were used for the influenza knowledge graph (KG). Throughout this study, various graph algorithms were utilized to extract significant nodes and relationship features and multiple influenza detection machine learning (ML) models were compared. Node classification and link prediction methods were employed on a multi-layer perceptron (MLP) model. Furthermore, the hyperparameters of the model were automatically tuned. The proposed MLP model demonstrated the lowest rate of loss and the highest specificity, accuracy, recall, precision, and F1-score compared to state-of-the-art ML models. Moreover, the Matthews correlation coefficient was promising. This study shows that graph data science can improve MLP model detection and assist in discovering hidden connections in influenza KG.
Keywords: Influenza detection, knowledge graph, graph multi-layer perceptron model, graph algorithms, automatic tuning, real-time analysis
DOI: 10.3233/JIFS-233381
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
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