The Impact of Socio-Health Factors Towards Life Expectancy across Countries using Machine Learning

Abstract

Life expectancy is a critical indicator of societal well-being and quality of life. Discovering and discussing the numerous factors that contribute to variations in life expectancy is crucial. Understanding these factors is important for shaping policies and interventions aimed at improving population health. With the advancement of the technology, prediction using machine learning is one of the alternatives in discovering the predictive factors that impact life expectancy. Therefore, the objective of this study is to identify and analyse the socio-health factors influencing life expectancy across countries using machine learning techniques. The study found that age group, immunisation status, and the presence of diseases such as HIV/AIDS were significant predictors of life expectancy. These insights are important for policymakers’ public health strategies and resource allocation.