Can a neuro-fuzzy integrated system accurately diagnose coronary heart disease and identify patients with high/low cardiac risk?
A neuro-fuzzy integrated system shows potential utility in the automated diagnosis and risk stratification of coronary heart disease based on simulation data.
Computational intelligence combines fuzzy systems, neural network and evolutionary computing. In this paper, Neuro-fuzzy integrated system for coronary heart disease is presented. In order to show the effectiveness of the proposed system, Simulation for automated diagnosis is performed by using the realistic causes of coronary heart disease. The results suggest that this kind of hybrid system is suitable for the identification of patients with high/low cardiac risk.
Ansari et al. (Thu,) studied this question.