Mining the risk: early cardiovascular detection in workers

ML models effectively predict clinically relevant BMI and BG risk transitions in the extraction of occupational health data. The use of longitudinal visit pairs and scenario-based evaluation improves the capacity of the models to achieve high AUC values and maintain accuracy and sensitivity, while ensuring generalization and consistency. These findings highlight the potential of this approach to improve the assessment of CVR and support preventive decision-making in high-risk working populations.