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Predicting Somatization Risk Among Infectious Disease Disaster Frontline Nurses Using Machine Learning.

Sung-Hee Shin^1,2, Eun Kyoung Yun^1,2

¹College of Nursing Science, Kyung Hee University, Seoul, South Korea.

Computers, Informatics, Nursing : CIN

|April 21, 2026

Summary

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Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Frontline nurses facing infectious diseases experience high somatization risk, influenced by infection anxiety and staffing issues. Machine learning identified key predictors for early intervention and enhanced nurse well-being.

Area of Science:

Occupational Health
Psychiatry
Machine Learning in Healthcare

Background:

Frontline nurses are crucial during infectious disease outbreaks.
High somatization poses a significant risk to nurse well-being and healthcare system resilience.
Identifying risk factors for somatization is vital for targeted interventions.

Purpose of the Study:

To identify key risk factors for high somatization among nurses during infectious disease responses.
To apply machine learning models for predicting somatization risk in this population.
To inform the development of proactive occupational health strategies.

Main Methods:

A cohort of 222 nurses in South Korea completed surveys on sociodemographic factors, work conditions, job stress, and somatic symptoms.

Keywords:

COVID-19 job stress machine learning somatization

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Machine learning models (Logistic Regression, XGBoost, Random Forest) were trained to predict high somatization risk (13.1% prevalence).

Model performance was evaluated using cross-validation, with feature importance analysis conducted across models.

Main Results:

XGBoost achieved the highest AUC (0.734), but Logistic Regression was chosen for interpretability.
Infection Anxiety and Delayed Staffing Assignment were the most consistent predictors of high somatization risk.
Satisfaction with salary/bonus and daily hours using personal protective equipment (PPE) also emerged as significant factors.

Conclusions:

Machine learning can effectively identify frontline nurses at high risk for somatization.
Targeted interventions addressing infection anxiety and staffing issues are recommended.
Proactive support is essential for nurse well-being and healthcare system resilience during public health crises.