ABSTRACT: Epidemic outbreaks on sea-going vessels pose a significant health and safety risk, particularly in isolated maritime environments lacking professional medical staff. The COVID-19 pandemic exposed major deficiencies in the preparedness and response protocols of maritime operations, where vessels became de facto quarantine units without access to structured medical decision support. This study presents the development and evaluation of DESSEV, a decision support system designed to assist maritime personnel in identifying and managing infectious disease outbreaks onboard vessels. A comprehensive non-SQL knowledge base was constructed using international epidemiological guidelines (WHO, CDC, ECDC) and medical literature, encompassing 22 infectious diseases and 35 symptoms categorized into 8 clinical domains. To support probabilistic diagnostics, artificial patient profiles were generated reflecting real-world symptom distributions. Three predictive models—Decision Tree, Naive Bayes, and Random Forest were trained. Their performances were assessed through cross-validation and random sampling techniques. Evaluation on a holdout test set of real patient cases showed that the Random Forest model achieved superior performance across all The Random Forest algorithm was thus selected as the core prediction engine for the DESSEV application. While not a substitute for professional medical care, DESSEV improves situational awareness and supports early risk mitigation actions aboard vessels. Future work will focus on integrating real-time health telemetry and user feedback to further refine diagnostic accuracy and usability.

KEYWORDS: Infectious Disease Prediction, Maritime Decision Support Systems, DESSEV Application, Random Forest Classifier, Medical Knowledge Base, Symptom-Based Diagnosis, Clinical Decision Support (CDSS), Maritime Health Security

REFERENCES