Prediction of heart abnormalities using Particle Swarm Optimization in Radial Basis Function Neural network

Early detection and prediction of fatal heart diseases can be useful for medical diagnosis and rehabilitation. In this study, a hybridized Particle Swarm Optimization (PSO) and Radial Basis Function Neural network (RBFN) model has been proposed to improve cardiac arrhythmia prediction accuracy. The performance of RBFN is very sensitive to parameter such as spread. In RBFN training process, the spread is optimized using PSO. In the proposed method a set of linear and non-linear features are extracted from the RR interval time series datasets, which are derived from the MIT-BIH arrhythmia database. Experiments are carried out on the heart rate (RR Interval) time series to predict the eight types of cardiac signals that contain normal and abnormal rhythms. The obtained results suggest that PSO-RBFN model may be an efficient tool to achieve enhancements in terms of prediction accuracy for diagnosis of cardiac disorders. The hybrid PSO-RBFN model yielded an overall prediction accuracy of 96.3%.

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