## 1 Introduction
Therefore, a knowledge-based system that integrates clinical domain knowledge of the experts in modeling the nonlinear casual relationship between the antecedent factors and dependent outcome under different types of uncertainties is more suitable for CDSS. The belief rule-based expert system (BRBES) employed in this paper develops an intelligent CDSS to predict the severity of COVID-19 in patients as it meets the above-mentioned requirements for CDSS. It has the capability of dealing with uncertainties both in knowledge representation and reasoning. Besides, it integrates the advantage of data driven and knowledge driven approach into a single framework with learning capability to approximate nonlinear functions and works as a universal estimator [8]. In BRBES, a hybrid knowledge representation scheme known as belief rule base (BRB) is employed to store the vague, random, fuzzy, incomplete, and imprecise clinical domain knowledge of experts in the form of belief rule to establish a nonlinear casual relationship between medical antecedents and consequent. The inference process to find the prediction outcome for illness severity of COVID-19 in a patient is implemented taking uncertain information of antecedent. The inference mechanism of BRBES is implemented using evidential reasoning approach (ER) that combines the activated rules in the BRB to reach a conclusion based on observed uncertain clinical data for a specific patient. ER possess the capability of rationally reserving the uncertainties in BRB and reflecting their effect on the final decision [17]. The initial BRB is constructed taking suggestion from the doctors who provided the parameters of each belief rule including the referential values of the antecedent attributes, antecedent-attribute weights, rule weights, and the belief degrees in the consequent part. However, doctors may not be able to accurately determine these parameters due to the limitations of human knowledge [18]. Therefore, an improved differential evaluation (IDE) optimization algorithm is proposed to fine tuned these parameters with accumulated patient data. Research shows that _lactic dehydrogenase (LDH), lymphocytes,_ and _high-sensitivity C-reactive protein (hs-CRP)_ are the three most efficient indicators for the disease severity for all COVID-19 variants [3; 4; 7; 19]. Therefore, in the proposed CDSS, these three features are used to predict the severity of COVID-19 in a patient. The effectiveness of the proposed system is verified by comparing its performance with state-of-the-art machine learning algorithms using a real patient dataset. The result shows that the prediction performance of BRBES is better than traditional machine learning algorithms. The rest of the paper is organised as follows. Section 2 gives an overview of the related works. Section 3 explains the belief rule-based expert systems (BRBES) methodology along with the optimization of BRBES using differential evaluation algorithm. The knowledge base construction, experimental setup, and the performance measure metrics are presented in Section 4. Section 5 includes the results and discussion, while Section 6 concludes the article.