Hybrid Approach to Predict the Death Rate of COVID-19 Patients

ABSTRACT

From closedown of December 2019, coronavirus has directly exhibited a lofty rate of transmission, coercing the World Health Organization to contend in the month of March 2020 that this unbeknownst coronavirus can be depicted as a pandemic. COVID-19 epidemic has guided to an operatic misplacement of deathly life over the public and presents an unbeknownst complaint to public fitness. It also affects the food systems of the person and the world of work. Once the person is infected by COVID, the metabolic exertion of vulnerable cells in his or her body is enhanced, similar as the one driven by COVID-19. The country's dietary habits are analyzed to predict the particular person's death rate. By using KNN algorithm, the performance metrics such as accuracy, precision, recall, and F1 score are evaluated for the country's dietary habits. In this research, both clustering and classification are combined to increase the accuracy of the prediction of death rate of the person. K-means is used for the clustering of the countries, and KNN is used for classifying the countries. The 170 countries are clustered based on the country's dietary habits, and other disease affected rate using K-means clustering algorithm. Countries are clustered into high and normal death rate countries based on the country's dietary habits and another cluster into high and normal death rate based on the other disease affected rate rather than COVID-19. Using the country's dietary habits and other disease affected clusters, the death rate of the person is predicted. After clustering the data based on the country's dietary habits and other disease affected rate, the KNN algorithm is used to classify and identify the person's death rate. Using clustering and classification algorithms in a combined way, an accuracy of 79% is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.