Heart disease detection using hybrid of bacterial foraging and particle swarm optimization

  • Padmavathi KoraEmail author
  • Ajith Abraham
  • K Meenakshi
Original Paper


Bacterial-foraging-optimization (BFO) has newly raised and one of the most useful nature inspired optimization algorithm for real parametric optimization. During the process of random walk, the BFO algorithm makes search in the random direction, which increases delay. To overcome the delay in reaching the global optimum and also to boost up the performance of BFO, we proposed an algorithm by mixing the features of BFO and particle swarm optimization (PSO) for detecting the abnormal cardiac beat. Computer simulations illustrate the usefulness of the developed approach compared to the basic versions of BFO and PSO. The main aim of the research is to develop new modifications of BFO and its combination with transform technique such as Wavelet Transform and machine learning method, support vector machines (SVMs) to test their performances in the detection of cardiac arrhythmia. Modification of BFO focuses for improving its convergence in terms of speed and accuracy. Provided results in this paper show that, for the detection of MI and BBB classes, the BFPSO algorithm with SVM gives 98.9% and 99.3% accuracy on MIT-BIH database by including NSR database also. Moreover, the results demonstrate the effectiveness of the proposed method to improve the detection of cardiac arrhythmia.


Electrocardiography (ECG) Bacterial foraging optimization (BFO) Particle swarm optimization (PSO) Support vector machine (SVM) 



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia
  2. 2.Machine Intelligence Research LabsWashingtonUSA

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