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An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty
Department of Computer Science and Engineering, Premier University, Chattogram 4000, Bangladesh.
Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.ORCID-id: 0000-0002-7473-8185
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-3090-7645
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-0244-3561
2021 (Engelska)Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 11, nr 13, artikel-id 5810Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

Ort, förlag, år, upplaga, sidor
Basel, Switzerland: MDPI, 2021. Vol. 11, nr 13, artikel-id 5810
Nyckelord [en]
belief rule base expert system, COVID-19 severity prediction, differential evolution, knowledge base system, optimization, machine learning
Nationell ämneskategori
Datavetenskap (datalogi) Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-86328DOI: 10.3390/app11135810ISI: 000672261800001Scopus ID: 2-s2.0-85109178041OAI: oai:DiVA.org:ltu-86328DiVA, id: diva2:1579695
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Validerad;2021;Nivå 2;2021-07-16 (johcin)

Tillgänglig från: 2021-07-10 Skapad: 2021-07-10 Senast uppdaterad: 2023-09-05Bibliografiskt granskad

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Hossain, Mohammad ShahadatIslam, Raihan UlAndersson, Karl

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