## 2 Related Work  
Artificial intelligent based clinical decision support systems have been successfully used in healthcare for various tasks including predicting risk of heart failure [20], mortality in pneumonia [21; 22], mortality risk in critical care [23; 24; 25]. These expert systems assist medical experts to understand and evaluate clinical findings better. Therefore, they can choose an optimal decision in a critical situation. In this study, we proposed a knowledge based expert system using a belief rule base to predict the clinical severity prediction of COVID-19. There are very few similar studies in the literature about the clinical severity prediction of COVID-19. The authors of [4] used machine learning techniques to predict the clinical severity of coronavirus. The data used in this study were collected from Wenzhou Central Hospital and Cangnan People's Hospital in Wenzhou, China, and are not privately accessible. First, three feature selection methods--information gain, gini-index, and chi-square statistics--were used to select the eleven most important clinical features. Six different machine learning algorithms--logistic regression, k-nearest neighborhood (KNN), two different decision trees, random forests, and support vector machines (SVM)--were applied. The performance of the classifiers was evaluated with only accuracy values. Best accuracy was obtained with SVM classifier with 80%. This method do not integrate the expert knowledge in the prediction process. Moreover, the accuracy is not very high to be applicable in the hospitals. In the another study [26], the authors applied machine learning classifiers to predict COVID-19 diagnosis. Clinical data was obtained from Hospital Israelita Albert Einstein at Sao Paulo Brazil. 18 clinical findings were considered in the study and classifiers were evaluated with AUC, sensitivity, specificity, F1- score, Brier score, positive predictive value, and negative predictive value. Five different classifiers were applied including, SVM, random forests, neural networks, logistic regression, and gradient boosted trees. The best AUC score was obtained with both SVM and random forest classifiers: 0.847. In [27], a clinical predictive model for COVID-19 was proposed. In the study, data were collected from Hospital Israelita Albert Einstein at Sao Paulo, Brazil, like in this study and in [26]. The authors applied various machine learning applications, including Random Forest (RF), Neural Network (NN), Logistic Regression (LR), Support Vector Machine (SVM), eXtreme Gradient Boosting(XGBoost), and determined the performance of classifiers by calculating sensitivity, specificity, and AUC scores. The best performance was obtained with XGB with 66% AUC score. The dataset used in this study is very small in size and the prediction accuracy is very low. The authors of [28] compared the performance of six deep learning models including Deep Neural Network (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), CNNLSTM, and CNNRNN to predict COVID-19 infection. Data used in this study were collected from Hospital Israelita Albert Einstein at Sao Paulo, Brazil, like in [26]. The dataset contains 18 laboratory findings of only 600 patients, where 520 patients were normal and 80 patients were COVID positive. The performance of the each of the predictive models was assessed in terms of accuracy, f1-score, precision, recall, and area under ROC curve (AUC) using both 10-fold cross-validation and train-test split approaches. Eighteen of the most important clinical features, which have a vital role in COVID-19 disease according to the studies in [4; 26; 27], were considered to develop the predictive models. The best performance was achieved with LSTM with an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. The data set used in this study is very small. Moreover, expert knowledge is not take into consideration in the decision-making.