Perceived health and life expectancy in EU countries: A machine learning approach for small samples
Year: 2026
Type: article
Abstract: Health represents a fundamental dimension of individual well-being, but it is equally significant when examined at the societal level, where individual and collective health outcomes are intrinsically interrelated. This study aims to analyse health across the 27 European Union Member States by investigating the impact of various types of determinants and assessing the potential for generating reliable predictions of health indicators. In the first part of the analysis, we focus on the population's self-perceived health status, exploring how different data processing strategies can enhance the performance of machine learning algorithms, particularly in the context of small sample sizes. In the second part, we replicate the same methodological approach using an objective health indicator—life expectancy at birth—in order to compare and contrast the findings. This comparison offers insights into the effectiveness and robustness of the methodological framework applied, and allows for broader reflections on the interplay between subjective and objective measures of health.
Source: Statistical Journal of the IAOS
Authors: Simona Cafieri, Francesco Petrucci
Institution: Sapienza University of Rome
Language: English
Topic: Quality of Life Measurement
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7128498774