An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

Abstract

Abstract-In all the regions of the world, heart failure is common and on raise caused by several aetiologies. Although the development of the treatment is fast, there are still lots of cases that lose their lives in emergence sections because of slow response to treat these cases. In this paper we propose an expert system that can help the practitioners in the emergency rooms to fast diagnose the disease and advise them with the appropriate operations that should be taken to save the patient's life. Based on the mostly binary information given to the system, Bayesian Network model was selected to support the process of reasoning under uncertain or missing information. The domain concepts and the relations between them were building by using ontology supported by the Semantic Web Rule Language to code the rules. The system was tested on 105 patients and several classification functions were tested and showed remarkable results in the accuracy and sensitivity of the system.

BNPA: An R package to learn path analysis input models from a data set semi-automatically using Bayesian networks

“…A literature review on different approaches was performed to identify studies about CVD using machine learning techniques such as BN, PA and SEM. The authors [17][18][19][20][21][22][23][24] developed models using Bayesian network techniques. PA models were created in the studies [25][26][27].…”