<p>Viruses like COVID-19 (Coronavirus Disease 2019) are highly contagious. Therefore, it is necessary to identify patients likely to test positive for such diseases to prevent their spreading. COVID-19 has been said to be a life-threatening pandemic because of its huge effects on the internal organs of the human body. The disease proves to be more severe to those who have any history of chronic diseases like hypertension, diabetes, malignancy, heart diseases, etc.; according to the literature, predictions for COVID-19 disease can be made using machine learning approaches based on various underlying medical conditions and parameters. There has been significant research on predicting the intensity and consequences of COVID-19. In the present study, we have proposed a machine learning-based approach to classify COVID-19-positive patients with various underlying medical conditions (chronic lung diseases, diabetes, hypertension, heart diseases, and malignancy). The model has been trained on a dataset of patients with known COVID-19 status and their underlying medical condition parameters (patient category, age, and gender). The dataset was acquired from the Indian Council of Medical Research (ICMR) repository for research purposes. The current study has employed several machine learning techniques, like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), and Extreme Gradient Boosting (XG Boost) on the dataset. Based on the experimentation and its results, significant improvement in model accuracy up to 98% has been seen. The results state that the underlying medical conditions play a major factor in the fact that individuals with these conditions are more vulnerable to catching the disease than normal people.</p>