Ontology-based intelligent decision support systems: A systematic approach

Abstract Intelligent and smart health monitoring is prevalent nowadays with the support of advancement in Internet of Things, machine learning, and ontology-based decision support systems. As a decision support system can analyze current patient vitals based on historical data, effective data representation from different data sources into a common knowledge base is essential. Web semantics has an increasingly important role to play here in terms of storing data following ontology for more usable knowledge repository. The findings of the decision support system can be fed to doctor’s smartphone as a message based on which the doctor may intervene in a specific scenario or may validate his own diagnosis with the one provided by the decision support system. As the comfort and convenience of the end-users of remote healthcare is important, in addition to quality of service, quality of experience is a matter of concern among other issues and challenges. This work emphasizes on several Machine Learning (ML) algorithms, ontology techniques to design and implement intelligent decision support system for effective healthcare support satisfying quality of service and quality of experience requirements.