Prediction of the number of COVID-19 confirmed cases using the hybrid FUCOM-Pareto analysis- random forest method

Abstract

After the COVID-19 epidemic, which emerged in December 2019 and is still in effect, almost all countries had to implement strict measures to control the spread of the virus. The ability of experts to reduce the spread primarily depends on the determination of the criteria affecting the spread. The fact many criteria that affect the rate of spread of COVID-19 and the most effective criteria cannot be determined, causes the spread, and therefore the number of positive cases and deaths to increase. Therefore, in the study;firstly, the weights of the criteria affecting the rate of spread were determined by using the full consistency method (FUCOM), which is a multi-criteria decision-making method, and the criteria that most affected the spread were determined by Pareto analysis, based on the criteria weights obtained. Then, based on the criteria obtained, the number of confirmed cases was predicted using the random forest method. The performance criteria values of the random forest were compared with different artificial intelligence methods such as artificial neural network, decision tree and support vector machine. Random forest gave the best results with error values (RMSE (3247), MAE (1714) and RRSE (0.374)). In addition, the random forest achieved a high prediction success of 92.9%.