Analysis of Obesity Based on Various Indicators by Countries via Linear Regression

Abstract

Obesity, a prevalent disease, begins with the Third Industrial Revolution. Obesity not only lowers numerous individuals’ quality of life, but also affects budgets in the health sector across the country and around the world. Obesity was once considered an exclusive problem among the rich: In general, high-quality meat is expensive, and plants (grains) have been relatively affordable. This proposition held true until the late 20th century, but today, obesity is no longer just a concern for the affluent. Factors such as climate change, supply-chain problems, high consumption demands, and more expensive materials such as feed and biofuels result in a price surge. Increased food prices have a direct impact on households, making it more difficult for the lower-income class to access nutritious foods. As a result, the poor reduce their consumption of nutritious foods such as fruits, vegetables, and grains and replace meals with inexpensive, artificial meat, and ultra-processed foods. This paper proposes policies by analyzing and predicting the degree of obesity using linear regression, a data mining technique, according to the development or welfare level of the country using various indicators by country. This paper refines and evaluates data through a preprocessing method after data collection and concludes by explaining the results.