Pronóstico de la esperanza de vida para la población de Colombia utilizando machine learning

Abstract

Introduction: This study evaluated life expectancy in Colombia, analyzing sociodemographic, macroeconomic, and health determinants using machine learning algorithms to identify significant patterns in the data. Objective: To forecast life expectancy in Colombia using machine learning models based on socioeconomic and demographic factors. Materials and Methods: A dynamic life expectancy model was developed using machine learning algorithms, integrating variables related to demography, economy, and health, thus overcoming the limitations of traditional mortality tables. Results: The Gradient Boosting Machines model demonstrated the highest accuracy, providing reliable projections at both the national and gender levels. The results offer a comprehensive overview of the evolution of life expectancy in Colombia and its main determinants. Conclusion: Life expectancy in Colombia is influenced by demographic, economic, educational, and health factors. It is essential to implement public policies aimed at reducing structural inequalities and ensuring essential services, particularly in rural and vulnerable communities.