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

2018 ◽  
Vol 337 ◽  
pp. 562-582 ◽  
Author(s):  
Abdollah Amirkhani ◽  
Elpiniki I. Papageorgiou ◽  
Mohammad R. Mosavi ◽  
Karim Mohammadi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Medical Decision ◽  
Modeling Uncertainty ◽  
Medical Decision Support ◽  
Learning Capabilities

scholarly journals A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Seyed Abbas Mahmoodi ◽  
Kamal Mirzaie ◽  
Maryam Sadat Mahmoodi ◽  
Seyed Mostafa Mahmoudi
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Healthcare Professionals ◽  
Fuzzy Cognitive Maps ◽  
Medical Decision ◽  
Risk Level ◽  
Medical Decision Support

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

XAI-based Medical Decision Support System Model

2020 ◽  
Vol 10 (12) ◽  
pp. 598-607
Author(s):  
Vytautas Petrauskas ◽  
Gyte Damuleviciene ◽  
Algirdas Dobrovolskis ◽  
Juozas Dovydaitis ◽  
Audrone Janaviciute ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Model ◽  
Medical Decision ◽  
Medical Decision Support
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