Evaluation of Dietary Habits in Relation to Covid-19 Mortality Rate Using Machine Learning Techniques

ABSTRACT

Coronavirus attacks have affected countless countries. The death rates between most countries are increasing day by day, and we have attempted to propose many considerations about the principal problems that cause dangerous infections across the globe. In this work, the dietary patterns of 170 countries are considered to identify correlations between diet practices and death rates, confirmed and recovered cases caused by COVID-19. We have used data from food intake by countries and data associated with the spread of COVID-19 and other health issues that help get new insights into the importance of nutrition and eating habits to combat the spreading of infectious diseases. We have built a machine learning model (regressor) such as ridge regressor, support vector regression, random forest, and XGBoost regressor to predict the mortality rate based on food intake information and Obesity. Two approaches were considered One with all food-related features taken as parameters and a simpler one, which reduced the dimensionality by using only two features Animal products and vegetal products. Both have issues (mainly of spread and non-linearity), but we could use different models and metrics. Next, we have built a model to predict obesity rates based on eating habits in each country. The proposed model was far more effective, and the general inclination of the information was taken and anticipated. We have also used data visualization approaches to get better insights into the data considered. © 2022, Success Culture Press. All rights reserved.