Impact of COVID-19 Pandemic on Diet Prediction and Patient Health Based on Support Vector Machine.

Recently, the COVID-19 pandemic has an efficient impact on all things around the world. Food estimation or diet has been grown great attention in the recent pandemic. This paper utilizes the Support Vector Machine (SVM) to predict the effect of the COVID-19 pandemic on a diet and further forecast the number of persons subject to death due to this pandemic. This work is based on the available dataset that contains fat quantity, energy intake (kcal), food supply quantity (kg), and protein for different categories of food. Furthermore, we are concerned the animal products, cereals excluding beer, obesity, including vegetal products that affect humans’ general health during the pandemic. Furthermore, the dataset includes confirmed deaths, recovered, and active cases in the percentage of each country’s current population. The results depend on Root Mean Square Error (RMSE), which indicates that SVM’s use with the Radial Basis Function (RBF) kernel produces 0.27. Further, SVM with linear Kernel achieves 0.18 RMSE, and finally, deep regression model achieves 0.29 RMSE.