Machine learning-based model to predict topics contributing to Sustainable Development Goals: A study of Latin American and European Countries

Barbara Sandra Lancho Barrantes

No cfk2g_v1, SocArXiv from Center for Open Science

Abstract: This paper examines publications related to the Sustainable Development Goals (SDGs) from Latin American and European countries, specifically focusing on whether the issues and challenges faced by these regions differ significantly. Latin American countries may be addressing region-specific health concerns or environmental challenges different from the European countries. A bibliographic universal classification of SDGs may overlook such crucial issues and nuances vital to the development of these countries. This study emphasises the differing priorities between Latin America and certain countries in the Global North regarding the health and well-being of all individuals. It focuses on improving reproductive, maternal, and child health, ending epidemics of major contagious diseases, reducing non-communicable diseases, promoting mental health and well-being, and addressing behavioral and environmental health risk factors. While Latin American countries often focus on topics like infections or mental health, the Global North tends to priorities Covid 19 and cancer. These differences reflect distinct economic, social, and political contexts, with us Global North sometimes overlooking the foundational needs of Global South countries. The research employs a sample from the Open Alex database, focusing on the ten most productive countries in Latin America and the ten most productive countries in Europe in SDG 3 Good Health and Well-being in the period of 2023-2024. The study, which falls under the framework of SDG 3 publications and employs data analytics techniques, identifies priority topics for Latin America and Europe, as well as common interests across both regions. Utilising predictive models based on machine learning, the study forecasts the topics that these countries are likely to prioritize in the near future with an accuracy of 60%. This level of accuracy may be influenced by the sample size used; therefore, future analyses will incorporate additional data, such as examining abstracts to determine whether European countries continue to diverge in their focus or if they find common ground on certain topics. This approach will provide valuable insights into regional priorities and the potential evolution of research related to the Sustainable Development Goals. The contrast between Latin America and Europe is valuable and quite original, since many global analyses tend to homogenize realities.

Date: 2025-10-03
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:cfk2g_v1

DOI: 10.31219/osf.io/cfk2g_v1

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