Multimedia Tools and Applications

, Volume 77, Issue 4, pp 4379–4399 | Cite as

Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

  • Gunasekaran Manogaran
  • R. Varatharajan
  • M. K. Priyan


Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.


Deep Learning Multiple Kernel Learning Adaptive Neuro-Fuzzy Inference System Machine Learning Heart Disease Diagnosis Big Data 


  1. 1.
    Agboizebeta IA, Chukwuyeni OJ (2012a) Application of neuro-fuzzy expert systemfor the probe and prognosis of thyroid disorder. Int J Fuzzy Logic Syst 2(2)Google Scholar
  2. 2.
    Agboizebeta IA, Chukwuyeni OJ (2012b) Cognitive neuro-fuzzy expert system forhypotension control. Comput Eng Intell Syst 3(6):21–32Google Scholar
  3. 3.
    Alamelumangai N, DeviShree J (2010) PSO aided neuro fuzzy inference system forultrasound image segmentation. Int J Comput Appl 7(14)Google Scholar
  4. 4.
    Alfarhan KA, Mashor MY, Saad M, Rahman A, Azeez HA, Sabry MM (2017) Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification. In: Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol 30. Trans Tech Publications, pp 1–11Google Scholar
  5. 5.
    Austin PC, Tu JV, Ho JE, Levy D, Lee DS (2013) Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J Clin Epidemiol 66(4):398–407CrossRefGoogle Scholar
  6. 6.
    Cao J, Yu S, Liu H, Li P (2016) Hand posture recognition based on heterogeneous features fusion of multiple kernels learning. Multimed Tools Appl 75(19):11909–11928CrossRefGoogle Scholar
  7. 7.
    Comak E, Arslan A, Türkoğlu I (2007) A decision support system based on support vector machines for diagnosis of the heart valve diseases. Comput Biol Med 37:21–27CrossRefGoogle Scholar
  8. 8.
    Dou W, Ruan S, Chen Y, Bloyet D, Constans JM (2007) A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image Vis Comput 25(2):164–171CrossRefGoogle Scholar
  9. 9.
    Ephzibah EP, Sundarapandian V (2012) An expert system for heart disease diag-nosis using neuro-fuzzy technique. Int J Soft Comput Artif Intell Appl 1(1)Google Scholar
  10. 10.
    Gijzen H (2013) Development: big data for a sustainable future. Nature 502(7469):38–38CrossRefGoogle Scholar
  11. 11.
    Guler I, Ubeyli ED (2005) Adaptive neuro-fuzzy inference system for classificationof EEG signals using wavelet coefficients. J Neurosci Methods 148(2):113–121CrossRefGoogle Scholar
  12. 12.
    Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL et al (2013) Big data and the future of ecology. Front Ecol Environ 11(3):156–162CrossRefGoogle Scholar
  13. 13.
    Jang SM, Hart PS (2015) Polarized frames on ―climate change‖ and ―global warming‖ across countries and states: evidence from twitter big data. Glob Environ Chang 32:11–17CrossRefGoogle Scholar
  14. 14.
    Jiang H, Tian Y (2011) Fuzzy image fusion based on modified Self-Generating Neural Network. Expert Syst Appl 38(7):8515–8523CrossRefGoogle Scholar
  15. 15.
    Khameneh NB, Arabalibeik H, Salehian P, Setayeshi S (2012) Abnormal red bloodcells detection using adaptive neuro-fuzzy system. Stud Health Technol Inform 173:30–34Google Scholar
  16. 16.
    Kumar PM, Gandhi UD (2017) A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. Comput Electr EngGoogle Scholar
  17. 17.
    Kumar KA, Punithavalli M (2011) Efficient cancer classification using fast adap-tive neuro-fuzzy inference system (FANFIS) based on statistical techniques. Int J Adv Comput Sci Appl Spec Issue Artif Intell 132–137Google Scholar
  18. 18.
    Kumar PM, Gandhi U, Varatharajan R, Manogaran G, Jidhesh R, Vadivel T (2017) Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things. Cluster Computing 1–12.
  19. 19.
    Lopez D, Gunasekaran M (2015) Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making. In: Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO-2015). Springer, pp 195–208Google Scholar
  20. 20.
    Lopez D, Gunasekaran M, Murugan BS, Kaur H, Abbas KM (2014) Spatial Big Data analytics of influenza epidemic in Vellore, India. In: Big Data (Big Data), 2014 I.E. International Conference on. IEEE, pp 19–24Google Scholar
  21. 21.
    Lopez D, Manogaran G (2016) Big Data Architecture for Climate Change and Disease Dynamics. In: Geetam S. Tomar et al (eds) The Human Element of Big Data: Issues, Analytics, and Performance. CRC PressGoogle Scholar
  22. 22.
    Lopez D, Manogaran G (2017) Parametric model to predict H1N1 influenza in vellore district. In: Handbook of Statistics, vol 37. Elsevier, Tamil Nadu, India, pp 301–316Google Scholar
  23. 23.
    Lopez D, Manogaran G, Jagan J (2017) Modelling the H1N1 influenza using mathematical and neural network approaches. Biomed Res 28(8):1–5Google Scholar
  24. 24.
    Lopez D, Sekaran G (2016) Climate change and disease dynamics-A Big Data perspective. Int J Infect Dis 45:23–24CrossRefGoogle Scholar
  25. 25.
    Luo Z, Wu M, Zhao Y (2015) Big Data Applications in Biomedical Informatics, (In Chinese). J Med Inform 36(5):2–9Google Scholar
  26. 26.
    Luo J, Wu M, Zhao Y (2016) Big Data Application in Biomedical Research and Health Care: A Literature Review. Biomed Inf Insights 8:1Google Scholar
  27. 27.
    Manogaran G, Lopez D (2017a) Disease surveillance system for big climate data processing and dengue transmission. Int J Ambient Comput Intell (IJACI) 8(2):88–105Google Scholar
  28. 28.
    Manogaran G, Lopez D (2017b) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng. doi:
  29. 29.
    Manogaran G, Lopez D (2017c). A Gaussian process based big data processing framework in cluster computing environment. Clust Comput :1–16Google Scholar
  30. 30.
    Manogaran G, Lopez D (2017d) A survey of big data architectures and machine learning algorithms in healthcare. Int J Biomed Eng Technol 25(3):182–211CrossRefGoogle Scholar
  31. 31.
    Manogaran G, Lopez D, Thota C, Abbas KM, Pyne S, Sundarasekar R (2017) Big data analytics in healthcare Internet of Things. In: Innovative Healthcare Systems for the 21st Century. Springer International Publishing, pp. 263–284Google Scholar
  32. 32.
    Manogaran G, Thota C, Kumar MV (2016) MetaCloudDataStorage architecture for Big Data security in cloud computing. Procedia Comput Sci 87:128–133CrossRefGoogle Scholar
  33. 33.
    Manogaran G, Thota C, Lopez D (2018) Human-Computer Interaction With Big Data Analytics. In: HCI Challenges and Privacy Preservation in Big Data Security. IGI Global, pp 1–22Google Scholar
  34. 34.
    Manogaran G, Thota C, Lopez D, Sundarasekar R (2017) Big Data Security Intelligence for Healthcare Industry 4.0. In: Cybersecurity for Industry 4.0. Springer International Publishing, pp 103–126Google Scholar
  35. 35.
    Manogaran G, Thota C, Lopez D, Vijayakumar V, Abbas KM, Sundarsekar R (2017a) Big Data Knowledge System in Healthcare. In: Internet of Things and Big Data Technologies for Next Generation Healthcare. Springer International Publishing, pp 133–157Google Scholar
  36. 36.
    Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017b) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting. Futur Gener Comput Syst 80(5):1–10Google Scholar
  37. 37.
    Manogaran G, Vijayakumar V, Varatharajan R, Kumar PM, Sundarasekar R, Hsu CH (2017c) Machine learning based big data processing framework for cancer diagnosis using hidden markov model and GM clustering. Wirel Pers Commun 1–18.
  38. 38.
    Mastorocostas PA, Hilas CS (2004) A dynamic fuzzy-neural filter for the analysis oflung sounds. IEEE Int Conf Syst Man Cybern 3:2231–2236Google Scholar
  39. 39.
    Mastorocostas PA, Theocharis JB (2005) A recurrent fuzzy-neural filter for real-time separation of lung sounds. Proc IEEE Int Joint Conf Neural Netw 5:3023–3028Google Scholar
  40. 40.
    Neagoe VE, Latin LF, Grunwald S (2003) A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis. In: AMIA Annual SymposiumProceedings, pp 494–498Google Scholar
  41. 41.
    Obi JC, Imainvan AA (2011a) Decision support system for the intelligient identi-fication of Alzheimer using neuro fuzzy logic. Int J Soft Comput 2(2):25–38CrossRefGoogle Scholar
  42. 42.
    Obi JC, Imainvan AA (2011b) Interactive neuro-fuzzy expert system for diagnosis ofleukemia. Global J Comput Sci Technol 11(12)Google Scholar
  43. 43.
    Ovreiu M, Simon D (2010) Biogeography-Based Optimization of Neuro-FuzzySystem Parameters for Diagnosis of Cardiac Disease. In: Genetic and Evo-lutionary Computation Conference (GECCO)‘10. Portland, pp. 1235–1242Google Scholar
  44. 44.
    Oweis RJ, Sunna MJ (2005) A combined neuro–fuzzy approach for classifyingimage pixels in medical applications. J Electr Eng 56(5–6):146–150Google Scholar
  45. 45.
    Polat K, Gunes K (2007) An expert system approach based on principal componentanalysis and adaptive neuro-fuzzy inference system to diagnosis of diabetesdisease. Digit Signal Process 17(4):702–710CrossRefGoogle Scholar
  46. 46.
    Priyan MK, Devi GU (2017) Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles. Clust Comput :1–15Google Scholar
  47. 47.
    Saeedi J, Faez K (2012) Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl Soft Comput 12(3):1041–1054CrossRefGoogle Scholar
  48. 48.
    Sengur A (2008) An expert system based on linear discriminant analysis and adap-tive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Syst Appl 35(1–2):214–222CrossRefGoogle Scholar
  49. 49.
    Son SY, Lee SH, Chung K, Lim JS (2015) Feature selection for daily peak load forecasting using a neuro-fuzzy system. Multimed Tools Appl 74(7):2321–2336CrossRefGoogle Scholar
  50. 50.
    Stavrakoudis D, Mastorocostas P, Theocharis J (2007) A pipelined recurrent fuzzy neural filter for the separation of lung sounds. Proc Fuzzy Syst Conf FUZZY-IEEE IEEE Int :1–6Google Scholar
  51. 51.
    Subasi A (2006) Automatic detection of epileptic seizure using dynamic fuzzy neuralnetworks. Expert Syst Appl 31(2):320–328CrossRefGoogle Scholar
  52. 52.
    Thota C, Manogaran G, Lopez D, Vijayakumar V (2017) Big Data Security Framework for Distributed Cloud Data Centers. In: Cybersecurity Breaches and Issues Surrounding Online Threat Protection. IGI Global, pp 288–310Google Scholar
  53. 53.
    Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018) Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System. In: Exploring the Convergence of Big Data and the Internet of Things. IGI Global, pp 141–154Google Scholar
  54. 54.
    Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI (2017) Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques. Comput Struct Biotechnol J 15:26–47CrossRefGoogle Scholar
  55. 55.
    Turkoglu I, Arslan A, Ilkay E (2002) An expert system for diagnosis of the heart valve diseases. Expert Syst Appl 23:229–236CrossRefGoogle Scholar
  56. 56.
    Ubeyli ED (2009) Adaptive neuro-fuzzy inference system for classification of ECGsignals using Lyapunov exponents. Comput Methods Prog Biomed 93(3):313–321CrossRefGoogle Scholar
  57. 57.
    Uguz H, Arslan A, Türkoğlu I (2007) A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases. Pattern Recogn Lett. Available online 11 October, 2006Google Scholar
  58. 58.
    Varatharajan R, Manogaran G, Priyan MK (2017a) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications 1–21.
  59. 59.
    Varatharajan R, Manogaran G, Priyan MK, Balaş VE, Barna C (2017b) Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimed Tools Appl :1–21Google Scholar
  60. 60.
    Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2017c) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust Comput :1–10Google Scholar
  61. 61.
    Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ (2017) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr EngGoogle Scholar
  62. 62.
    Vayena E, Salathé M, Madoff LC, Brownstein JS (2015) Ethical challenges of big data in public health. PLoS Comput Biol 11(2):e1003904CrossRefGoogle Scholar
  63. 63.
    Wang R, Du H, Zhou F, Deng D, Liu Y (2014) An adaptive neural fuzzy network clothing comfort evaluation model and application in digital home. Multimed Tools Appl 71(2):395–410CrossRefGoogle Scholar
  64. 64.
    Wen J, Chang XW (2017) Success probability of the Babai estimators for box-constrained integer linear models. IEEE Trans Inf Theory 63(1):631–648MathSciNetCrossRefzbMATHGoogle Scholar
  65. 65.
    Wen J, Zhou Z, Wang J, Tang X, Mo Q (2016) A sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit. IEEE Trans Signal ProcessGoogle Scholar
  66. 66.
    Xiao Y, Xia L (2016) Human action recognition using modified slow feature analysis and multiple kernel learning. Multimed Tools Appl 75(21):13041–13056CrossRefGoogle Scholar
  67. 67.
    Yu J, Duan H (2013) Artificial bee colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion. Optik-Int J Light Electron Optics 124(17):3103–3111CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Gunasekaran Manogaran
    • 1
  • R. Varatharajan
    • 2
  • M. K. Priyan
    • 1
  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  2. 2.Sri Ramanujar Engineering CollegeChennaiIndia

Personalised recommendations