A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting more than five millions of people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models, random forest, and multinomial Logistic regression to predict isolation, release, and decease states for COVID-19 patients in South Korea. The proposed approaches are evaluated using Data Science for COVID-19 (DS4C) dataset. An analysis for DS4C dataset is also provided. Experimental results and evaluation show that random forest outperforms other approaches with 99.63% in state prediction accuracy and 99.51% in F1-score.