Advancing Mortality Prediction in Ecuador Through Machine Learning Techniques

In recent years, the mortality rate in Latin America and the Caribbean has seen a steady increase. Ecuador has experienced a rising trend in mortality rates since 2014, with a 0.16% increase until 2019, followed by a remarkable 57.44% surge in 2020, primarily attributed to the COVID-19 pandemic. However, in 2021, the mortality rate decreased to 6.72%, marking a notable departure from the previous year. Effective healthcare policy planning demands accurate mortality projections, yet Ecuador needs official information regarding future mortality trends. This study addresses this gap by leveraging machine learning techniques to predict mortality patterns and investigating the underlying causes and trends to support informed governmental decision-making. The research rationale lies in the critical need for precise mortality data to guide public health policies effectively. Predictive models based on historical data are pivotal for anticipating mortality trends and comprehending their root causes. This, in turn, facilitates the development of targeted healthcare programs and policies that cater to the population's needs and ultimately enhance the overall quality of life. The overarching objectives of the study encompass analyzing the evolution of mortality rates and their causes in Ecuador. The findings of this study could furnish invaluable insights for the formulation and execution of evidence-based public policies in the realm of healthcare. This study exemplifies the utility of machine learning in mortality analysis and policy planning, demonstrating its capacity to inform governmental decisions and shape effective public health policies in the Ecuadorian context.