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Life sciences · Journal article
Frontiers in Public Health · September 16, 2026
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Objective Chronic diseases and cardiometabolic disorders are major public health and occupational health challenges in Taiwan, particularly among healthcare workers exposed to shift work, high occupational stress, and irregular schedules. Nurses and hospital staff are especially vulnerable to hypertension, type 2 diabetes mellitus, stroke, and cardiovascular disease risk. Using a localized digital risk assessment platform and machine learning–based data-driven modeling, this study aimed to evaluate chronic disease risk among medical center employees and identify high-risk occupational subgroups for precision health management. Participants/Methods This retrospective cross-sectional study included 1,916 employees aged 35–70 at a Northern Taiwan medical center from 2019 to 2024. Ten-year predicted risks for coronary artery disease, stroke, type 2 diabetes mellitus, hypertension, and major adverse cardiovascular events were estimated using the Chronic Disease Risk Assessment Platform. To assess associations between predicted risk and demographic or occupational variables, we used ordinal logistic regression, generalized linear models, and decision tree algorithms. Results Hypertension exhibited the highest overall predicted risk (38.6%), followed by type 2 diabetes mellitus and MACE. Male employees had higher odds of being classified into a higher predicted HTN risk category than female employees (odds ratio [OR] = 4.18, 95% confidence interval [CI]: 3.21–5.44, p < 0.001). By occupational category, nurses had higher odds of being in a higher predicted 10-year HTN risk category than administrative staff (OR = 1.32, 95% CI: 1.04–1.68, p = 0.024). Multivariable generalized linear models identified increasing age and metabolic factors, including obesity, elevated blood pressure, hyperglycemia, and dyslipidemia, as the major predictors of predicted cardiometabolic risk, whereas occupational category was not independently associated with disease risk after adjustment. Significant interaction effects were observed for CAD, stroke, HTN, and MACE. Ten-fold cross-validated decision tree models identified age, waist circumference, blood pressure, and body mass index as key classification variables, with overall accuracies ranging from 86.9 to 91.7% and F1-scores ranging from 0.622 to 0.936 across the five outcomes. Conclusion This study applied disease risk prediction models developed by the Taiwan Health Promotion Administration platform, integrating clinical indicators and machine learning analyses to identify healthcare workers at increased disease risk. The findings suggest that localized digital prediction tools may be integrated into institutional health surveillance systems to support continuous health monitoring, early risk identification, and proactive preventive care in healthcare settings.