A Comparative Approach To Predict Corona Virus Using Machine Learning

Coronavirus disease (COVID-19), is one of the most infectious diseases which reshaped our everyday lives globally in the 21^st century. Technology progressions have a rapid effect on every field of life, be it the medical domain or any other. More than 250 countries have been affected by COVID in no matter of time. The Indian government is making the necessary steps to control the spread of virus in the society. People all over the world are vulnerable to its consequences in the future. In a pandemic like this, people often worry whether they show a symptom of COVID-19 or not. Various AI methods have been applied successfully in epidemic studies. This paper presents the prediction and analysis of COVID-19 using various machine learning algorithms. In the present study, ML-based enhanced model is implemented to predict the possible threat of COVID-19 all over the world and the algorithms used in these models classifies the COVID patients based on several subsets of features and predicts their likeliness to get affected to this disease. This model uses 20 metrics including the patient's geographical location, travel history, health record statistics, etc., to predict the severity of the case and the feasible outcome. This research finds the patients exposed to Covid-19 and could be used as a reference, by the patients before consulting further with the doctor. The model developed using K-Nearest Neighbors (KNN) is effective with a prediction accuracy of 98.34%, Recall of 97% and an F1-Score of 0.97. Overall, this paper proposes a simple and practicable method to quickly identify and predict the high risk patients and provide priority to them for treatment so that the fatality rate can be decreased.