A SWIFT EXPOSURE OF SARS-COV-2 BY MFSM MODEL

The most epidemic of respiratory disorder is COVID-19 caused by new type of named as corona virus SARS-Cov2 is a rigorous global concern. Due to the lack of medical assistance for this situation effective treatment in the medical area the strategy of containment was the immediate action to be taken to get reduce the contagion by applying isolating the patients who are suffering with this virus. On the other hand isolation cannot be completely resolve and practically difficult to put in practice for a long time. To take faster decisions on healing with isolation will give better features to conclude for suspected infection cases. These predicted cases can be the best predictors for positive analysis. To do the analysis on patient characteristics, symptoms, diagnosis and outcomes. By using machine learning supervised algorithms a model is implemented to recognize the features of COVID-19 disease with best accuracies. Many supervised machine learning methods were been studied for the dataset considered in our analysis. The MFSM algorithm performed with the highest accuracy (>94%) to predict COVID-19 status for all age groups. Statistical analysis of for the collected symptoms is fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). Out of which 54.4% of patients did not have any symptoms.