Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators

Abstract

Machine learning algorithms play an important role in analyzing complex data in research across various fields. In this paper, we employ multiple regression algorithms and statistical techniques to investigate the relationship between objective and subjective quality of life indicators and reveal the key factors affecting happiness at the international level based on data from the Human Development Index and the World Happiness Index covering the period from 2015 to 2021. The Pearson correlation analysis showed that happiness is related to the HDI score and GNI per capita. The best-performing model for forecasting happiness was the random forest regression, with a R2 score of 0.93667, a mean squared error of 0.0033048, and a root mean squared error of 0.05748, followed by the XGBoost regression and the Decision Tree regression, respectively. These models indicated that GNI per capita is the most significant feature in predicting happiness.

Artificial Intelligence for Quality of Life Study: A Systematic Literature Review

“…The analysis of social media data provides a unique lens into public sentiment and interests, offers valuable insights into the collective consciousness of a society, and gives the ability to discern trends, concerns, and areas of focus within the broader community. (7) Through the use of general wellbeing indicators, policymakers can gain a holistic view of a nation's overall wellbeing and compare multiple countries. Examples of these indicators are included in this table.…”

“…However, a fair assessment of the quality of life (QoL), especially on the country level, must take into account more than just people's sentiments and individual satisfaction, but also other factors, particularly objective measures such as the economic dimension [6] [7].…”