Comparing the various ML models is essential to understanding their advantages and disadvantages so that they can be used effectively for tasks in the futureDespite the contributions of ML models to prediction tasks, the empirical evaluation of these models for prediction tasks is reliant on synthetic datasets, which is still an issue in the field; so, this work is informed. In this paper, we carried out an empirical study comparison between three variations of ML models, namely, Logistic Regression (LR), Decision Tree (DT)and Random Forest (RF) in predicting COVID-19. All models used in this study were evaluated with different metrics such F1-score, precision, recall, accuracy and confusion matrix. The study approach presents a complete prediction procedure of the COVID-19 status using patients readings dataset. It has four steps: collecting data, getting it ready, training the model, and testing the ML model. The results show all the ML models in this study performed very. Each model reported an accurate score of more than 0.9. Overall, RF outperformed the three ML models tested in this study. RF said that recall, precision F1 score, and accuracy were 096, 1, 0.98, and 0.98, respectively. RF outperforms other models in learning the pattern and association in datasets with sequential or temporal properties due to their inherent architecture for extracting correlation in the dataset.