Predicting nationwide obesity from food sales using machine learning

Dunstan, Jocelyn; Aguirre, Marcela; Bastías, Magdalena; Nau, Claudia; Glass, Thomas A; Tobar, Felipe

Keywords: databases and data mining, food sales, machine learning, obesity, supervised learning

Abstract

The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be accessed inexpensively through different suppliers on a regular basis. This study applies a methodology to predict obesity prevalence at the country-level based on national sales of a small subset of food and beverage categories. Three machine learning algorithms for nonlinear regression were implemented using purchase and obesity prevalence data from 79 countries: support vector machines, random forests and extreme gradient boosting. The proposed method was validated in terms of both the absolute prediction error and the proportion of countries for which the obesity prevalence was predicted satisfactorily. We found that the most-relevant food category to predict obesity is baked goods and flours, followed by cheese and carbonated drinks.

Más información

Título de la Revista: Health Informatics Journal
Fecha de publicación: 2019
Página de inicio: 146045821984595
Idioma: English
DOI:

10.1177/1460458219845959

Notas: ISI