## 2 Review Methodology
### Search Code Strategy  
The original research articles available from the beginning of time were collected from the databases (S.D., WoS, PubMed) on 16 March 2021. Many queries were used to enhance the search-related quality to various ML and DL techniques for detecting COVID-19. Important searching keywords used for the data retrieval were as follows: (a) SD database search code is TOPIC: ((\"Coronavirus\" OR \"COVID-19\") AND (\"Detection\" OR \"diagnosis\" OR \"classification\") AND (\"Machine learning\" OR \"Deep learning\")); (b) PubMed database search code is TOPIC: ((\"Novel Coronavirus\" OR \"COVID-19\" OR \"coronavirus\" OR \"2019-nCoV\" OR \"new coronavirus\" OR \"SARS-CoV-2\") AND (\"Detection\" OR \"diagnosis\" OR \"prediction\" OR \"prognosis\") AND (\"Machine learning\" OR \"Deep learning\"));(c) WoS database search code is TOPIC:(\"Novel Coronavirus\" OR \"COVID-19\" OR \"coronavirus\" OR \"2019-nCoV\" OR \"new coronavirus\" OR \"SARS-CoV-2\") AND (\"Detection\" OR \"diagnosis\" OR \"prediction\" OR \"prognosis\" OR \"Analysis\" OR \"Classification\") AND (\"Machine learning\" OR \"Deep learning\"). The published papers with these terms found in titles, abstracts, and keywords were retrieved. Moreover, only original research papers related to the detection of COVID-19 through ML/DL techniques were extracted, and the review articles and books were eliminated from the collection.  
Machine and Deep Learning Techniques  
Remarkable progress has been made in using different ML algorithms applied to medical datasets for detecting different diseases. ML is a mathematical and statistical technique that gives computers the ability to learn from data and elaborate a convoluted framework. DL consists of a group of algorithms applied to develop an expert system that can identify problems and yield predictions. These ML and DL techniques produce intelligence into a computer that can elicit the patterns relative to specific data and then proceeds for automatic reasoning [31]. Many ML/DL algorithms have been applied to predict, detect, and diagnose COVID-19 [32].  
In this article, we have presented an analysis of these ML/DL techniques used to detect COVID-19. Below is a brief description of these classifiers.  
SVM is a supervised ML classifier, a specified set of training examples given to SVM that learn the hyperplane, isolate the instances from each class, and magnify the edges among data instances and hyperplane. K-NN is also a supervised algorithm. It classifies the unknown sample by measuring its distance from other training samples and calculates K-smallest distances. The unknown sample output class label is given by most represented in these K classes [33].  
A Decision Tree classifier consists of one root node, numerous internal nodes, and several terminal nodes. D.T. represents a tree structure in which every leaf node is related to a group of features, and a branch displays a value. The primary purpose of the D.T. algorithm is to build a tree for the whole data and provide refine results on each leaf. A Random Forest classifier ensembles various decision trees for prediction, which are developed by grasping numerous classification trees simultaneously R.F. performance is more superior to a single tree [34]. This classifier can be employed efficiently, and it accomplishes predictions for many higher dimensional datasets with greater accuracy.  
CNN and D.N.N. is a deep learning technique consisting of several layers such as input, output, and hidden layers. These layers modify the data with convolution filters. Currently, researchers are employing these deep learning techniques to detect COVID-19 [31].  
It is investigated in the literature that these models achieved greater accuracy in detecting viral pneumonia, bacterial pneumonia, and COVID-19 [26], respectively. ML/DL represents a tremendous innovation in automatic diagnostic classification systems; therefore, these classifiers present a suitable choice to be employed in diagnosing COVID-19.