Analysis and Prediction of Life Expectancy at the Global Level and in Selected Countries

Abstract

The relationship between life expectancy and healthcare costs has been the subject of numerous studies by various organizations. This is because it serves as a key tool for evaluating the effectiveness of the healthcare sector, as it affects both the national budget and the quality of life and life expectancy of ordinary citizens. The purpose of this study is to contribute to the advancement of this research by incorporating additional parameters and applying combinations of data analysis techniques. Initially, the differences between selected and representative countries were presented, but basically this analysis focused on predicting life expectancy using a set of economic, health, and environmental indicators. To achieve this goal, two data sets from the World Bank’s open data platform were used for the period 2000–2023. The study involved handling of missing values, performing correlation analysis, applying a linear regression model, conducting principal component analysis, and creating a regression model using the random forest method. The process was implemented using the Python programming language. Finally, the summary results were presented and recommendations for future research were formulated. This comprehensive effort is intended to serve as an advisory tool for relevant decision-making and provides the basis for further analysis of the topic.