Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification

  • Vijayashree JayaramanEmail author
  • H. Parveen Sultana
Original Research


Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for avoiding the risk factors. Then the earlier detection system has been created by utilizing optimized and hybridized techniques to recognize the heart disease in earlier stage. So, artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system. Initially, heart disease related information is collected from Heart Disease Data Set-UCI repository. The collected information is huge in dimension which is difficult to process, that reduces the efficiency of heart disease identification system. So, the dimensionality of the features are reduces according to the behavior of gravitational cuckoo search algorithm. The selected features are processed by above defined associative memory classifier. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results.


Heart disease Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network Heart disease data set-UCI repository 



  1. Al Shalabi L, Shaaban Z (2006) Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: International conference on dependability of computer systems in IEEE, 207–214Google Scholar
  2. Bhatia S, Prakash P, Pillai GN (2008) SVM based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features. In: Proceedings of the world congress on engineering and computer scienceGoogle Scholar
  3. Boros E, Hammer P, Ibaraki T, Kogan A, Mayoraz E, Muchnik IB (2000) An implementation of logical analysis of data. IEEE Trans Knowl Data Eng 12:292–306CrossRefGoogle Scholar
  4. Bouckaert RR, Frank E (2004) Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai H, Srikant R, Zhang C (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture notes in computer science, vol 3056. Springer, Berlin, Heidelberg, pp 3–12Google Scholar
  5. Bryll R, Gutierrez-Osuna R, Quek F (2003) Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognit 36(6):1291–1302CrossRefzbMATHGoogle Scholar
  6. Butler-Yeoman T, Xue B, Zhang M (2015) Particle swarm optimization for feature selection: a hybrid filter-wrapper approach. In: IEEE congress on evolutionary computation (CEC)Google Scholar
  7. Chai TY, Woo SS, Rizon M, Tan CS (2010) Classification of human emotions from EEG signals using statistical features and neural network. In IJIE 1:1–6Google Scholar
  8. Dilmac K (2013) A new ECG arrhythmia clustering method based on modified artificial bee colony algorithm, comparison with GA and PSO classifiers. In: IEEE international symposium on innovations in intelligent systems and applications (INISTA)Google Scholar
  9. Durairaj M, Revathi V (2015) Prediction of heart disease using back propagation MLP algorithm. Int J Sci Technol Res 4(08):235–239Google Scholar
  10. Ganesan V (2010) Application of neural networks in diagnosing cancer disease using demographic data. Int J Comput Appl (0975–8887)Google Scholar
  11. Geng Y, Zhang L, Sun Y, Zhang Y, Yang N, Wu J (2016) Research on ant colony algorithm optimization neural network weights blind equalization algorithm. Int J Secur Appl 10(2):95–104. Google Scholar
  12. Ghwanmeh S (2012) Applying advanced NN-based decision support scheme for heart diseases diagnosis Sameh Ghwanmeh. Int J Comput Appl (0975–8887) 44(2):37–41Google Scholar
  13. Han J et al (2009) Data mining concepts and techniques, 2nd Edn. Elsevier, LondonGoogle Scholar
  14. Jabbar A, Deekshatulu B, Chandra P (2013) Heart disease classification using nearest neighbor classifier with feature subset selection. Anale Seria Informatică XI fasc:1Google Scholar
  15. Kabira MM, Shahjahan M, Murase K (2012) A new hybrid ant colony optimization algorithm for feature selection. Expert Syst Appl 39(3):3747–3763CrossRefGoogle Scholar
  16. Kołodziej M, Majkowski A, Rak RJ (2012) Linear discriminant analysis as EEG features reduction technique for brain-computer interfaces. PrzeglądElektrotechniczny (Electrical Review) 88:NR 3aGoogle Scholar
  17. Kumar Y, Sahoo G (2014) A review on gravitational search algorithm and its applications to data clustering and classification. IJ Intell Syst Appl 06:79–93Google Scholar
  18. Mahajan R, Bansal D, Singh S (2011) A real time set up for retrieval of emotional states from human neural responses. Int J Biomed Biol Eng 8(3):144–149Google Scholar
  19. Manikandan G, Sairam N, Sharmili S, Venkatakrishnan S (2013) Achieving privacy in data mining using normalization. Indian J Sci Technol 6(4):4268–4272Google Scholar
  20. Nawi NM, Rehman MZ, Khan AA (2014) New bat based back-propagation (BAT-BP) algorithm. In: Advances in intelligent systems and computing book series, vol 240. AISC, ChennaiGoogle Scholar
  21. Oullette R, Browne M, Hirasawa K (2004) Genetic algorithm optimization of a convolutional neural network for autonomous crack detection. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753)Google Scholar
  22. Ranganatha S, Pooja Raj HJ, Anusha C, Vinay SK (2013) Medical data mining and analysis for heart disease data set using classification techniques. In: Research and technology in the coming decades (CRT 2013). UCI.
  23. Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42:186CrossRefGoogle Scholar
  24. Shouman M, Turner T, Stocker R (2011) Using decision tree for diagnosing heart disease patients. In: Proceedings of the 9-th Australasian data mining conference (AusDM’11), Ballarat, AustraliaGoogle Scholar
  25. Sridhar KP, Baskar S, Shakeel PM et al (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Human Comput. Google Scholar
  26. Tan PN et al (2009) Introduction to data mining, Pearson education, fourth impressionGoogle Scholar
  27. Vadicherla D, Sonawane S (2013) Classification of heart disease using Svm and ANN. Int J Res Comput Commun Technol 2(9):694–701Google Scholar
  28. Wan W, Birch JB (2013) An improved hybrid genetic algorithm with a new local search procedure. J Appl Math 2013:10. ID 103591)MathSciNetGoogle Scholar
  29. Wu H, Kim S, Bae K (2010) Hidden Markov model with heart sound signals for identification of heart diseases. In: Proceedings of 20th international congress on acoustics, ICA 2010Google Scholar
  30. Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009). IEEE Publications, Piscataway, pp. 210–214 (arXiv:1003.1594v1)Google Scholar
  31. Zhong L, Wan J Z, Huang J, Cao G, Xiao B (2013) Heart murmur recognition based on hidden Markov model. J Signal Inf Process 4:140–144Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia

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