A neuro-fuzzy decision support model for therapy of heart failure
by Okure U. Obot; Faith-Michael Uzoka; Oluwole Charles Akinyokun; Joseph J. Andy
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 6, No. 4, 2014

Abstract: Most medical decision support systems focus on diagnosis with little emphasis on therapy to the effect that though an accurate diagnosis is undertaken patients still have problems of drug misuse as a result of inaccurate therapy. The purpose of this paper is to design an assistive model for therapy of heart failure using artificial neural networks (ANN). Artificial neural networks have been found to be a very veritable tool in learning from existing datasets and based on the results, can perform accurate prediction on the data it has not encountered before through generalisation. It was based on this that 134 datasets on heart failure were collected from three hospitals and trained in a feed forward back propagation learning neural networks. This was further refined through the fuzzy system and some decision support filters. Results obtained from the neuro-fuzzy system indicate that the model has the ability to refine and enhance the physician's ability to prescribe an appropriate therapy based on the diagnosis. This study is one of the few attempts at utilising soft computing technology in the diagnosis and therapy of cardiovascular diseases. The authors had previously developed neuro-fuzzy models for diagnosis of heart failure.

Online publication date: Fri, 31-Oct-2014

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