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A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty

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  • Amirkhani, Abdollah
  • Papageorgiou, Elpiniki I.
  • Mosavi, Mohammad R.
  • Mohammadi, Karim

Abstract

In this paper, an active Hebbian learning (AHL) for intuitionistic fuzzy cognitive map (iFCM) is proposed for grading the celiac. This method performs the diagnosis procedure automatically, and it is more suitable for specialists in better understanding and assessment of the disease. Our approach shows potential in confronting hesitancy through considering experts’ uncertainty in modeling. In this study, we propose an automatic computer-aided diagnosis system based on iFCMs to determine the grade of celiac disease. By relying on the knowledge of experts, the key features of disease are extracted as the main concepts, and the iFCM model for the complex grading system is designed as a graph with eight concepts. The results obtained by applying our proposed method (iFCM-AHL) on the dataset verify the ability and effectiveness of this model. The proposed iFCM by considering hesitation of experts in modeling process and property of less sensitive to missing input data, not only increase accuracy in detecting the type of disease, but also obtain a higher robustness, in dealing with incomplete data. The obtained results have been compared with the findings of the FCM, interval type-2 fuzzy logic system, untrained iFCM and five extensions of the FCM. Comparative results show that our approach offers a robust classification method that produces better performance than other models.

Suggested Citation

  • Amirkhani, Abdollah & Papageorgiou, Elpiniki I. & Mosavi, Mohammad R. & Mohammadi, Karim, 2018. "A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 562-582.
    • Handle: RePEc:eee:apmaco:v:337:y:2018:i:c:p:562-582
    DOI: 10.1016/j.amc.2018.05.032
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    Citations

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    Cited by:

    1. Hosseinpour, Mahsa & Ghaemi, Sehraneh & Khanmohammadi, Sohrab & Daneshvar, Sabalan, 2022. "A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    2. Themistoklis Koutsellis & Georgios Xexakis & Konstantinos Koasidis & Alexandros Nikas & Haris Doukas, 2022. "Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps," Operational Research, Springer, vol. 22(5), pages 5733-5763, November.
    3. Linmao Ma & Jing Yu & Long Zhang, 2019. "An Analysis on Barriers to Biomass and Bioenergy Development in Rural China Using Intuitionistic Fuzzy Cognitive Map," Energies, MDPI, vol. 12(9), pages 1-23, April.
    4. Ghaboulian Zare, Sara & Alipour, Mohammad & Hafezi, Mehdi & Stewart, Rodney A. & Rahman, Anisur, 2022. "Examining wind energy deployment pathways in complex macro-economic and political settings using a fuzzy cognitive map-based method," Energy, Elsevier, vol. 238(PA).
    5. Chen, Chen-Tung & Chiu, Yen-Ting, 2021. "A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

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