MACHINE LEARNING-BASED CLINICAL DECISION SUPPORT SYSTEM

This article aims to design a self-organizing decision support system for early detection of important physiological events. The proposed consists pre-processing, clustering and diagnostic systems based on fuzzy logic modelling. Clustering methods have been used in empirical pattern analysis. Especially when the available information is incomplete or data model subject ambiguity. applies primarily medical/clinical data. A engine can be viewed as unsupervised learning from given set. module analyses patient's vital signs identify relationships, patterns, clusters among medical Then, use modelling detect symptoms events early. Based results, we observed high degree agreement between system's interpretation human expert's diagnosis abnormal sign detection. Keywords: Patient monitoring; Early clinical events; Clinical system; Self-organising Machine learning;