Perceived health and life expectancy in EU countries: A machine learning approach for small samples
Simona Cafieri & Francesco Petrucci
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.