In this project, we present a robust methodology for evaluating the fitness of the Semi-Naïve Bayesian Network (SNBN) disease model structure used by Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification (GUARDIAN)*a real-time, scalable, extensible, automated, knowledge-based infectious disease detection and diagnosis system*for determining the appropriate thresholds, which separate between different 'goodness of fit' categories, and for tuning the model parameters to optimize its applicability within a particular healthcare facility.