What question did this study set out to answer?

The aim is to identify the primary determinants of seafarers' mental health using a machine learning framework.

April 15, 2026INQUIRY The Journal of Health Care Organization Provision and Financing1 citationsOpen Access

Work-Life Conditions as the Primary Determinant of Seafarer Mental Health: An Explainable Machine Learning Analysis

YJYongwei JiangChina-Japan Friendship Hospital ZTZhendong TangDalian Maritime University HLHuan LiuWannan Medical College

Key Points

The aim is to identify the primary determinants of seafarers' mental health using a machine learning framework.
Survey of 500 seafarers conducted to gather data on mental health and risk factors.
Machine learning model created using a Random Forest classifier and SHAP for feature interpretation.
Preprocessing included label encoding and stratified sampling for training and test datasets.
Model performance evaluated through metrics such as accuracy, precision, recall, and F1-score.
Work-life conditions were identified as the most significant determinant of mental health.
Favorable environmental conditions enhanced positive impacts of career development and social recognition.
High onboard service pressure consistently harmed mental health, even in optimal conditions.
The framework emphasizes the need for systemic environmental improvements and tailored psychological support.

Abstract

Seafarer mental health has become an increasingly critical concern due to its substantial implications for transportation safety and operational performance. Traditional analytical approaches often inadequately capture the complex, non-linear interactions among the multidimensional determinants involved. To bridge this gap, this study proposes an explainable machine learning (ML) framework that integrates a Random Forest classifier with SHapley Additive exPlanations (SHAP) for simultaneous prediction and interpretation. Based on a survey of 500 seafarers, 12 risk factors were preprocessed through label encoding and the dataset was split into training and test sets using stratified sampling. A Random Forest model, optimized via Bayesian hyperparameter tuning, was employed to predict psychological states, with performance evaluated through accuracy, precision, recall, and F1-score. SHAP analysis was then applied to quantify global feature importance and to examine individual prediction mechanisms and interaction effects. The results identify current work-life conditions as the most influential determinant, exhibiting a polarizing effect on psychological states that substantially outweighs other factors. Furthermore, favorable environmental conditions amplify the positive effects of career development and social recognition, whereas high onboard service pressure persistently undermines mental health even under otherwise optimal circumstances. These findings underscore the necessity of prioritizing systemic environmental improvements as a foundation for effective psychological interventions, suggesting that tailored support strategies should be implemented subsequent to such enhancements. This study provides a data-driven, interpretable framework to support precision mental health management in maritime operations. These findings advocate for maritime policymakers and shipping companies to prioritize systemic improvements in onboard living and working conditions as a foundational strategy, complemented by targeted psychological support and enhanced awareness of mental health services.

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