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Machine Learning-XGBoost Analysis of Subjective Well-being Among Chronic Hepatitis B Patients

Year: 2025

Type: conference-paper

Abstract: This study examined the relationships among objective social support, subjective social support, social participation, self-efficacy, and subjective well-being in chronic hepatitis B (CHB) patients using the XGBoost machine learning algorithm. Data were collected from 253 CHB patients. Using an optimal hyperparameter search, the XGBoost model achieved a classification accuracy of 98.04%. The results indicated that objective support, subjective support, self-efficacy, and social participation significantly predicted subjective well-being. XGBoost also highlighted self-efficacy as the most crucial predictive factor. These findings emphasize targeted psychological interventions to enhance self-efficacy and social support among CHB patients.

Source: International Conference on Computer and Multimedia Technology (ICCMT)

DOI: 10.1145/3757749.3757776

Authors: Lala Li, Jie Zhou

Institutions: Chinese Academy of Sciences, Institute of Psychology, Chinese Academy of Sciences, University of Chinese Academy of Sciences

Language: English

Topic: Psychological Well-being and Life Satisfaction

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4414182469

FWCI: 2.061

Cites: 14

Cited by: 1

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