TABLE II. ML Articles Describing the Dataset, the Author's Identity, the Nation of Publishing, the Method Used in the Article, and the Findings for Analyzing the COVID-19 Disease.

S. No.	Author	Country	Datasets used	Methods applied	Task and Algorithm used	Results
1	Khanday et al. [38]	India	212 reports by GitHub	ML algorithm	Logistic regression, Naive Bayes and classification used	According to the study, logistic regressions and multinomial Nia"ve Bayes are 96% more accurate than commonly used algorithms.
2	Burdick et al. [39]	USA	197 patients of United States health systems	Support vector Kuhntucker model	Logistic regression, classification	Their findings revealed that this algorithm has a higher diagnostic odds ratio (12.58) for anticipating ventilation and effectively triaging patients than a comparator early warning system, such as the Modified Early Warning Score (MEWS), which had (0.78) sensitivity, whereas this algorithm had (0.90) sensitivity, resulting in higher specificity (p 0.05), It also indicates that it is capable of accurately identifying 16% more patients than a commonly used scoring method, resulting in fewer false positive findings.
3	Varun et al. [40]	USA	Total reported cases are 184 319	ML algorithm	Convolutional neural networks, classifications	In response to the crisis, New York City's medical and academic centres issued a call to action to AI researchers to leverage their electronic medical record data to better understand SARS-COV-2 patients. Due to a shortage of ventilators and a reported need for a quick and accurate method of triaging patients at risk for respiratory failure, our goal was to develop a machine-learning algorithm for frontline physicians in the emergency department and inpatient floors to better risk-assess patients and predict who would require intubation and mechanical ventilation.
4	Luca et al. [41]	Italy	85 dataset of chest X-rays	ML algorithm	K-nearest neighbors’ classifier	Authors present a method for automatically detecting COVID-19 disease by analyzing medical photos in this publication. We use supervised ML methods to develop a model using 85 chest X-rays that are freely available for research reasons. The experiment demonstrates that the proposed technique is efficient in distinguishing between COVID-19 disease and other lung diseases.
5	Constantin et al. [42]	Germany	500 chest CTs dataset and 152 datasets of COVID-19 patients	Support vector Kuhntucker model	Convolutional neural network, classifications	The researchers discovered that combining ML with a clinically embedded software platform allowing for speedier development, deployment, and adoption in medical practise. Finally, they developed a fully automated lung segmentation and opacity measurement approach that was ready for medical usage and performed at human levels even in difficult situations in just ten days.
6	Lamiaa et al. [43]	Egypt	5000 cases of COVID-19	ML algorithm	Linear regression model	The results demonstrated that the specified models, such as exponential, 4, 5, and 6 degree polynomial regression models, are brilliant, especially the 4 degree model, which will aid the government in planning operations for one month. They also included a well-known log that will rise through the regression model, resulting in the epidemic peak and end in 2020. There is also a final report on the total number of COVID-19 patients.
7	Dan et al. [44]	Israel	Total 6995 patients in Sheba Medical Centre	Support vector Kuhntucker model	Artificial neural network and classifications	The most relevant variables in the models were the APACHE II score, white blood cell count, time from onset of symptoms to admission, oxygen saturation, and blood lymphocytes count. Machine-learning algorithms exhibited excellent efficacy in predicting significant COVID-19 when compared to the most effective strategies available. As a result, artificial intelligence might be utilised to accurately predict COVID-19 patient risk, enhance patient triage and in hospital allocation, better prioritise medical resources and improve overall COVID-19 pandemic management.
8	Joep et al. [45]	Netherlands	Total 319 patients	Gradient Boosting algorithm	Logistic regression and classification	The CO-RADS scoring system on chest CT provides a sensitive and specific approach for diagnosing COVID-19, especially if RT–PCR testing are rare during an outbreak. Combining a predictive machine-learning model with diagnostic chest CT for COVID-19 could increase accuracy even more. To improve the model, they look into more possible predictors. However, because up to 9% of RT–PCR positive patients are not diagnosed by chest CT or our ML model, RT–PCR should remain the gold standard of testing.
9	Christopher et al. [46]	Germany	Total 368 independent variables	ML algorithm	Naive Bayes and Classifications	They mainly focused on variables and factors that increasing COVID-19 incidence in Germany, using the multimethod ESDA technique, which also provides an appropriate insight into spatial and spatial nonstationaries of COVID-19 occurrence. Variables like infrastructure, built environment densities, and socioeconomic factors all showed a link with COVID-19 after being examined on a county level in Germany. Their findings suggest that avoiding needless travel and social isolation can be effective approaches to limit contamination.
10	Hoyt et al. [47]	U.S.	Total 290 patients	Support vector Kuhntucker model	Logistic Regression and Classification	In the entire population, the findings revealed no link between mortality and therapy, although hydroxychloroquine was connected to a statistically significant (p = 0.011) improvement in survival, with an adjusted hazard ratio of 0.29 and a CI of 0.11–0.75. Despite the fact that the algorithm predicted an adjusted survival of 82.6% in the treated group and 51.2% in the untreated group, the algorithm detected a 31% improvement in the COVID-19 population after ML applications, demonstrating the critical role of ML in medicine.
11	María et al. [48]	International	Food for each of the 170 countries	ML algorithm	K-means clustering	According to the data, countries with the highest death rates consume more fats, whereas those with the lowest death rates consume more grains and have a lower overall average calorie intake.
12	Shinwoo et al. [49]	USA	Toal 790 Korean immigrants	ML algorithm	Artificial neural network, classifications	Artificial neural network (ANN) analysis, a statistical model capable of investigating complex nonlinear interactions of variables, was applied. The algorithm has properly predicted a person's flexibility, familiarity with everyday discernments, and racial actions toward Asians in the United States since the beginning of the COVID-19 epidemic, offering critical advice for public health. practitioners
13	Yigrem et al. [50]	Southern Ethiopia	Total 244 samples	ML algorithm	Logistic regression, classification	More than half of the study participants reported coronavirus disease-related stress, showing that there is a strong association between COVID-19-related stress and health-care employees.
14	Abolfazl et al. [51]	USA.	Total database of 57 candidate from the US Centres for Disease and Control and Johns Hopkins University	ML algorithm	ANN, classification	According to Getis-Ord Gi, the results showed that the supplied model (logistic regression) demonstrated that these components and factors define the presence/absence of the COVID-19 hotspot in a geographic information system (p 0.05). As a result, the findings were useful in identifying the impact of potential risk variables connected to COVID-19 for public health decision-makers.
15	Rustam et al. [52]	Pakistan	Time series COVID-19 database	LR, LASSO, SVM, ER	Texture data are used as input and supervised learning such as linear regression, LASSO Regression, support vector machine, exponential smoothing used	ES outperforms all other models, followed by LR and LASSO, which are also good at projecting new confirmed instances.
16	Sharma [53]	India	CT Image database	Residual neural network	Image data are used as input and custom vision software of Microsoft azure based on ML techniques is used	91% accuracy achieved
	Peng, Nagata [54]	Brazil	various countries COVID-19 data	Support Vector Regression (SVR)	Text data are used as input and support vector regression and kernel functions used	It is clear that caution is required when using ML.
17	Ardabili et al. [55]	Germany	5 countries COVID data	MLP, ANFIS	Time-series data as an input and genetic algorithm and particle swarm optimization and supervised learning algorithm is used	High generalization
18	Nemati et al. [56]	USA	1182 hospitalized patients COVID-19 dataset	SVM	Text data are used as input	Significant results have been achieved in predicting recovery time
19	Sun et al. [57]	USA	COVID-19 patients’ data of Massachusetts, Georgia, and New Jersey	Gradient boosting algorithm	Texture data are used as input	Better prediction rate
20	Burdick et al. [58]	USA	COVID-19 Patient Dataset	ML algorithm	Text data are used as input and ML and MEWS used	Good prediction rate
21	Kavadi et al. [59]	India	Indian COVID-19 Dataset	Support vector Kuhntucker model	Text data are used as input and propose a partial derivative regression and nonlinear ML (PDR-NML) method is used	Better prediction rate
22	Banerjee et al. [60]	U.K.	D-19 data from Midstream	ANN	Text data are used as input	A higher rate of infection detection prediction is attained.
23	Wang et al. [61]	China	COVID-19 Data	Logistic model + prophet method	Time-series data as an input and Fb Prophet model used	Good prediction rate
24	Han et al. [62]	China	CT datasets	AD3D-MIL algorithm (A Deep 3D-Multiple Instance Learning)	Image data are used as input and attention-based deep 3-D multiple instance learning (AD3D-MIL) is used	An accuracy of 97.9% is obtained
25	Vaid et al. [63]	Canada	JHU CSSE database	developed a ML model to uncover hidden patterns based on reported cases and to predict potential infections.	Text data are used as input	Good prediction rate
26	Elaziz et al. [64]	Egypt	Two chest X-ray COVID-19 dataset	KNN +Manta-Ray Foraging Optimization	Image data are used as input and CNN used	For two datasets, accuracy of 96.09% and 98.09% was obtained.
27	Ahamad et al. [65]	Bangladesh	Patient COVID-19 data	Extreme Gradient Boosting, Decision Tree, RF, SVM, Gradient Boosting Machine	Text data are used as input and Random Forest, XGBoost, Gradient Boosting Machine and SVM is used	XGB outperformed other proposed methods
28	Brinati et al. [66] Hasan [67]	Italy	time series COVID-19 dataset	Ensemble empirical mode	Text data are used as input and support vector machines and random forest algorithm used	Better prediction rate
		Wuhan		Decomposition (EEMD) + ANN)
29	Farid et al. [68]	Egypt	CT images COVID-19 dataset	SVM, NB, CNN, RF, as well as JRIP	Image data are used as input and Composite hybrid feature extraction (CHFS) used	The proposed CHFS has a higher prediction rate than CNN.
30	Shaban et al. [69]	Egypt	CT images COVID-19 dataset	Enhanced KNN	Image data are used as input and Genetic Algorithm (GA) and KNN classifier is used	Good detection rate
31	Ou et al. [70]	China	Pandemic COVID-19 data	Neural network	Text data are used as input and support vector machines and RF algorithm used	Good identification rate
32	Samuel et al. [71]	USA	COVID-19 dataset	LR, Naive Bayes (NB), Linear regression (LiR), KNN	Text data are used as input and logistics regression (LR) and KNN is used	NB outperformed other techniques
33	Pinter et al. [72]	Germany	COVID-19 dataset of Hungary data	Adaptive network-based fuzzy inference system and Multilayered perceptron-imperialist competitive algorithm	Text data are used as input and adaptive network-based fuzzy inference system and multilayered perceptron-imperialist competitive algorithm are used	Good prediction rate
34	Carrillo-Larco and Castillo-Cara. [73]	U.K.	COVID-19 patients’ data	K-Means algorithm	Text data are used as input and unsupervised ML used	Better classification rate
35	Benıtez-Pena et al. [74]	Spain	patients’ COVID-19 data	RF and Support Vector Regression (SVR)	Text data are used as input	High prediction rate
36	Zhong et al. [75]	China	patient COVID-19 blood sample data	SVM, KNN, RF, LR	Text data are used as input	Better severity detection
37	Yadav et al. [76]	India	COVID-19 Synthetic dataset	SVR	Text data are used as input and support vector regression (SVR) is used	Polynomial regression, SVR outperformed LiR,
38	Chang et al. [101]	Australia	COVID-19 dataset	ABM approach	Australian Census-based epidemic model	Agent based modelling using a fine-grained computational simulation applied
39	Zhang et al. [102]	Africa	Africa CDC dataset	PHSM data (Oxford COVID-19 Government response tracker dataset)	Text data are used as input	Descriptive analyses were done to establish the different cases
40	Andrikopoulos and Greg [103]	Australia	COVID-19 dataset	Australian Centre for behavioral research in diabetes, diabetes Australia adapted a resource developed	Text data are used as input	It is clear that people with diabetes are at greater risk of serious health impacts in pandemics such as COVID-19 than people without diabetes