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A Fuzzy Cognitive Map Learning Approach for Coronary Artery Disease Diagnosis in Nuclear Medicine

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Fuzzy Logic and Technology, and Aggregation Operators (EUSFLAT 2023, AGOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14069))

Abstract

Coronary artery disease (CAD) is the primary cause of death and chronic disability among cardiovascular conditions worldwide. Its diagnosis is challenging and cost-effective. In this research work, Fuzzy Cognitive Maps with Particle Swarm Optimization (FCM-PSO) were used for CAD classification (healthy and diseased). In particular, a new DeepFCM framework, which integrates image and clinical data of the patients is proposed. In this context, we employed the FCM-PSO method enhanced by experts’ knowledge, along with an efficient attention Convolutional Neural Network, to improve diagnosis. The proposed method is evaluated using 571 participants and achieved 77.95 ± 5.58% accuracy, 0.22 ± 0.05 loss, 76.98 ± 8.27% sensitivity, 77.39 ± 7.13% specificity, and 73.97 ± 0.09% precision, implementing a 10-fold cross-validation process. The results extracted from the proposed model demonstrate the model’s efficiency and outperform traditional machine learning algorithms. An essential asset of the proposed DeepFCM framework is the explainability, as it offers nuclear physicians’ meaningful causal relationships between clinical factors regarding the diagnosis.

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Acknowledgments

The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers” (Project Number: 3656).

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Authors and Affiliations

  1. Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larissa, Greece

    Anna Feleki, Ioannis D. Apostolopoulos, Konstantinos Papageorgiou, Elpiniki I. Papageorgiou & Nikolaos I. Papandrianos

  2. Department of Nuclear Medicine, School of Medicine, University General Hospital of Patras, University of Patras, 265-00, Patras, Greece

    Dimitris J. Apostolopoulos

Authors
  1. Anna Feleki
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  2. Ioannis D. Apostolopoulos
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  3. Konstantinos Papageorgiou
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  4. Elpiniki I. Papageorgiou
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  5. Dimitris J. Apostolopoulos
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  6. Nikolaos I. Papandrianos
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Corresponding author

Correspondence to Nikolaos I. Papandrianos .

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Editors and Affiliations

  1. University of the Balearic Islands, Palma, Spain

    Sebastia Massanet

  2. University of Oviedo, Gijón, Spain

    Susana Montes

  3. University of the Balearic Islands, Palma, Spain

    Daniel Ruiz-Aguilera

  4. University of the Balearic Islands, Palma, Spain

    Manuel González-Hidalgo

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Feleki, A., Apostolopoulos, I.D., Papageorgiou, K., Papageorgiou, E.I., Apostolopoulos, D.J., Papandrianos, N.I. (2023). A Fuzzy Cognitive Map Learning Approach for Coronary Artery Disease Diagnosis in Nuclear Medicine. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_2

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  • DOI: https://doi.org/10.1007/978-3-031-39965-7_2

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  • Online ISBN: 978-3-031-39965-7

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