An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty
| Autor: | Ahmed, Faisal, Hossain, Mohammad Shahadat, Islam, Raihan Ul, Andersson, Karl |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer Sciences differential evolution QH301-705.5 Physics QC1-999 knowledge base system Medieteknik Engineering (General). Civil engineering (General) belief rule base expert system Chemistry Datavetenskap (datalogi) machine learning Media and Communication Technology TA1-2040 Biology (General) optimization QD1-999 COVID-19 severity prediction |
| Zdroj: | Applied Sciences, Vol 11, Iss 5810, p 5810 (2021) Applied Sciences Volume 11 Issue 13 |
| ISSN: | 2076-3417 |
| Popis: | 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. Validerad;2021;Nivå 2;2021-07-16 (johcin) |
| Databáze: | OpenAIRE |
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