## 2 Related Work
The performance and robustness of the machine learning-based radiomic model for different levels of COVID-19 severity prediction are investigated in [29]. A chest CT image dataset of 1110 COVID-19 patients is used in this study to classify the severity of each patient into mild, moderate, and severe. One-hundred-and-seven radiomic features are extracted from each CT for application of machine learning algorithm. The dataset was randomly divided into training and holdout validation set consisting of 60% and 40% of data, respectively. After selecting the most significant features using the statistical method, a logistic regression model is applied predicting severe cases from mild and moderate cases. The predictive power of the models is measured using the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. Their predictive model can predict mild from severe COVID-19 with AUC = 0.85 and moderate from severe COVID-19 with AUC = 0.65. Machine Learning-based radiomic models proposed in this study were moderately sensitive to inter-observer classifications, and thus cannot be used without any caution. In [30], the authors proposed an interpretable machine learning method using the random forest algorithm for severe and non-severe COVID-19 clinical type classification. They used 26 symptom features and 26 biochemistry features collected from a cohort of 214 non-severe and 148 severe type COVID-19 patients from Wuhan, China, to train and validate the machine learning model. Their proposed method achieved 90% and 95% predictive accuracy when symptom and biochemistry features are used independently as input. Further, they extracted the five most significant features from each modality applying the Gini impurity feature selection method. Combining these top ten features from both modalities their proposed method achieved 99% predictive accuracy. However, collecting biochemistry features is costly, especially for a country like Bangladesh. Therefore, it cannot be used as an initial screening tool for triaging in Bangladesh. In [31], the authors utilized several machine learning algorithms to build the COVID-19 severeness detection model based on urine and blood tests. The data were collected from 137 clinically confirmed cases of COVID-19 who were hospitalized at Tongji Hospital between the date 18 January 2020 to 13 February 2020. Seventy-five of these patients were severe and 62 were normal. Clinically obtained values including age, sex, body temperature, heart rate, respiratory rate, blood pressure, and the blood/urine test data were used as features in this study. Using all these features, five machine learning models were then built applying Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K nearest neighbor (KNN), and AdaBoost algorithms. Among these models, the Support Vector Machine (SVM) exhibited superior performance with an overall accuracy of 0.7926. Next, using the Student \(t\)-test, 32 most important features whose \(p\)-value were less than 0.05 were selected. Four features were further removed for inter-feature redundancies. The final SVM model was trained using 28 features and achieved an overall accuracy of 0.8148. However, the dataset used in this study is small in size and not balanced. Moreover,the prediction accuracy is not satisfactory to be used as a screening tool in hospitals. A summary of the related works are shown in the Table 1.