<![CDATA[Delays in the emergency department triage process often precipitate severe clinical deterioration, exposing a critical vulnerability in orthodox healthcare management. While the proliferation of Machine Learning (ML) promises computational efficiency, purely unsupervised models like K-Means frequently misclassify extreme clinical anomalies due to the dominance of imbalanced historical data. This study engineered a Clinical Decision Support System (CDSS) utilizing a hybrid architecture that synthesizes K-Means clustering with a deterministic rule-based heuristic. Operating on a simulated medical dataset extracting variables such as age, heart rate, body temperature, and oxygen saturation (SpO2), the methodology standardized feature weights before executing a dual-layer validation. The system intercepts life-threatening parameters (e.g., SpO2 < 90%) through pre-defined clinical thresholds, bypassing algorithmic bias, while delegating stable cases to the spatial grouping logic of K-Means. Empirical testing on simulated triage scenarios demonstrated that the hybrid model eradicated the critical misclassification inherent in standalone unsupervised algorithms, achieving absolute alignment with the Emergency Severity Index (ESI) standards. The resultant cross-platform application, deployed via a Flutter desktop interface and a Python-Flask backend, operationalizes this logic to minimize human error and drastically reduce diagnostic response time.]]>
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