A medical decision support system for predicting the severity level of COVID-19

Mohsen Abbaspour Onari, Samuel Yousefi, Masome Rabieepour, Azra Alizadeh, Mustafa Jahangoshai Rezaee (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
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The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms’ impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.
Original languageEnglish
Pages (from-to)2037-2051
Number of pages15
JournalComplex and Intelligent Systems
Issue number4
Publication statusPublished - Aug 2021
Externally publishedYes


  • COVID-19
  • Medical decision support system
  • Severity level prediction
  • Evidence-based paired relationships
  • Data-driven Bayesian network
  • Fuzzy cognitive map


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