Ontology Based System Architecture to Predict the Risk of Hypertension in Related Diseases

Hypertension is a main public health problem around the globe associated with high death rates. Many clinical decision support systems (CDSS) exist to assist doctors with decision making tasks in the domain of hypertension. Most of these systems are implemented using traditional database methodologies. These systems cannot handle huge amount of data sets. Analyzing, interpreting and processing of data are so difficult in traditional systems. They are not flexible and adaptable to complex requirements and processes and they lack intelligence. Medical knowledge must be represented in a meaningful way in order for the computers to analyze and acquire inferred data. Ontology is among the most powerful tools to encode medical knowledge semantically. The aim of this paper is to propose an ontology based decision support system model to predict the risk of hypertension and diabetics in related diseases. The proposed system uses ontologies as knowledge base, the patient medical profile to be stored in a semantic way and an inference mechanism to infer data in the decision making process. The main advantage of using ontologies in a CDSS is that, different sources can share and reuse the ontological concepts represented in a semantic way and thus integration of data is easier. The proposed system is targeted to implement in health centers in Sultanate of Oman.