Abstract
Introduction: Reliable simulation of complex physiological dynamics is crucial for effective medical education. Irregular Fuzzy Cellular Automata (FCA) are powerful modeling tools but suffer from instability in heterogeneous environments, limiting their pedagogical utility. This study introduces an adaptive sensitivity control mechanism to stabilize FCA, creating a robust framework for educational simulations.
Methods: A neighborhood index was developed to dynamically normalize membership functions, enhancing system adaptability. The model was evaluated using synthetic data mimicking clinical variables. Beyond technical stability, we assessed the framework’s capacity to maintain realistic scenarios in noisy environments, a key requirement for training accuracy.
Results: The proposed model reduced state fluctuations by 45% and increased accuracy by 60%, ensuring that simulated clinical trajectories remain biologically plausible for learners. Convergence time shortened by 35%, facilitating real-time interaction. These technical improvements translate to consistent training environments where students can reliably observe cause-and-effect relationships without artifactual instability.
Conclusion: This adaptive mechanism significantly enhances the reliability of FCA-based medical simulations. By providing a stable platform for modeling complex health data, it improves the educational validity of virtual training scenarios, fostering better critical thinking and decision-making skills in healthcare professionals.