Impact of Global Country Indicators on Life Expectancy

Abstract

Investigating the factors that influence life expectancy is crucial for improving public health, planning healthcare and social services, and pension provision. Understanding these factors enables the development and implementation of effective policies to enhance the quality of life. This article presents a study that analyzes data from 195 countries, covering demographic, economic, healthcare, and education factors, to identify those that impact life expectancy. Various models were experimented with, including linear regression, random forest, gradient boosting, SVM, and artificial neural networks. Different machine learning algorithms, combinations of features, and strategies for handling missing dataset values were tested in building the models. The best-performing model used linear regression. It was found that reproductive characteristics, the number of individuals enrolled in higher education, and the number of general practitioners had the highest correlation with life expectancy. Surprisingly, out-of-pocket health expenditure was not a leading factor in determining life expectancy.