Enhancing happiness and life satisfaction in university students: analysis with a machine learning approach.

ABSTRACT:

Background

Subjective well-being (SWB) is vital for the personal growth of university students. Machine learning approach have been increasingly used in identifying SWB predictors for their ability to capture complex and multidimensional predictors. Still, the feature selection is not often justified from a theoretical perspective.

Objective

Under the guidance of the conceptual model of psychology and public health, this study aims to apply machine learning to identify the top predictors of happiness and life satisfaction (LS) as the two components of SWB among a sample of university students.

Methods

This cross-sectional study analyzed university students from the China Family Panel Studies, including 816 participants from the 2022 wave for model development and 724

SUBMITTER: Long Q 

PROVIDER: S-EPMC12801446 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature