## 1 Introduction
This study is mainly focused on predicting the presence of COVID-19 in a person; thus, a supervised machine learning model had to be developed. Several machine learning methods were utilized in building disease prediction models (e.g., coronary artery disease, respiratory disease, breast cancer, diabetes, dementia, and fatty liver disease [9; 10; 11; 12; 13; 14]). The researchers devised a list of published disease prediction studies that utilized supervised machine learning algorithms, such as J48 Decision Tree (J48 DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (k-NN), and Naive Bayes (NB). More algorithms were utilized, such as Multi-layer Perceptron (MLP), Logistic Regression (LR) and Artificial Neural Network (ANN). Table 1 displays a list of disease prediction studies, machine learning algorithms utilized, the best algorithms according to the experiments performed, and the accuracy obtained. Several supervised machine learning methods were utilized in building a disease prediction model, and it is apparent that an algorithm can perform differently depending on the dataset. There were several research attempts in using machine learning for the early prediction of this novel disease. Based on the laboratory findings, a COVID-19 predictor has \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Disease Prediction** & **Machine Learning Algorithms** & **Best Algorithm** & **Accuracy Obtained** \\ \hline
[9] & Coronary Artery Disease & LR, DT, RF, SVM, k-NN, ANN & ANN & 93.03\% \\
[10] & Respiratory Disease & RF, DT, MLP & DT & 99.41\% \\
[11] & Breast Cancer & SVM, DT, NB, and k-NN & SVM & 97.13\% \\
[12] & Diabetes & DT, SVM, and NB & NB & 76.30\% \\
[13] & Dementia & J48 DT, NB, RF, and MLP & J48 DT & 99.23\% \\
[14] & Fatty Liver Disease & LR, k-NN, DT, SVM, NB, and RF & LR & 75.00\% \\ \hline Proposed Method & COVID-19 & J48 DT, NB, SVM, k-NN, and RF & SVM & 98.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning algorithms used in building a disease prediction model; the best algorithm and the accuracy obtained are displayed. been developed using Convolutional Neural Network (CNN); the researchers developed this predictor using the dataset from a hospital in Sao Paul, Brazil, and this model yielded 76% accuracy [15]. Another research attempt to improve COVID-19 severity prediction was undertaken using initial test results from the blood routine, which disclosed that age, white blood cells, lymphocytes, and neutrophils were the key factors in severity, and concluded that it could successfully improve the prediction accuracy of the model [16]. To accelerate the prediction of COVID-19, publicly available X-ray images were also used for training, and the best-performing model has been integrated into smart phones and evaluated. It was concluded that VGG16 generated a higher positive likelihood and lower negative likelihood in terms of predicting COVID-19 [17]. Moreover, another study used machine learning in predicting the mortality of a COVID-19 patient through the development of an XGBoost model, which successfully predicted the mortality of the patients earlier by about 10 days, having an accuracy of more than 90% [18]. Machine learning was also utilized in the development of models that can forecast cases of COVID-19. Using an algorithm called Stochastic Fractal search with a mathematical model, the number of life-threatening cases, deaths, and recovered, symptomatic and asymptomatic patients were predicted [19]. Predicting confirmed and death cases daily on the Asian continent was also made possible by using the Polynomial Neural Network method [20]. Using a prediction model in the study of [PERSON] et al. [21], the overall infection count and the days on which there will be a possible COVID-19 outbreak in Italy and Spain can be predicted one week before occurrence. These are some examples of studies that applied machine learning in predicting COVID-19 presence, cases, and death rates.