Neural Computing and Applications

, Volume 31, Supplement 1, pp 93–102 | Cite as

A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases

  • Subhashini NarayanEmail author
  • E. Sathiyamoorthy
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


Recently, using of the intelligent technologies in the field of clinical decision making is increased rapidly to improve the lifestyles of patients and to help for reducing the workload and cost concerned in their healthcare. Heart diseases are one of the primary causes of death. However, if the diseases are identified at the early stage, the rate of death can be decreased. Thus, the disease identification process has become a matter of concern. An efficient medical recommendation system has been proposed in this paper, namely Fourier transformation-based heart disease prediction system (FTHDPS) by using Fourier transformation and machine learning technique to predict the chronic heart diseases effectively. Here, the input sequences rely on the patient’s time series details or data, which are crumbled by Fourier transformation for extracting the frequency information. In FTHDPS, a bagging model is utilized for predicting the conditions of the patients in advance to produce the absolute recommendation. In FTHDPS, three classifiers are used, namely artificial neural network, Naive Bayes and support vector machine, and real-life time series chronic heart disease data are used to evaluate the proposed model. The experimental results demonstrate that FTHDPS is much efficient to provide a reliable and accurate recommendation to the heart patients.


Intelligent system Naive Bayes classifier Neural network Support vector machine Rough sets Heart disease 


Compliance with ethical standards

Conflict of interest

There is no conflict of interest between the authors to publish this manuscript.


  1. 1.
    Pu LN, Zhao Z, Zhang YT (2012) Investigation on heart risk prediction using genetic information. IEEE Trans Inf Technol Biomed 16(5):795–808CrossRefGoogle Scholar
  2. 2.
    Namasudra S (2017) An improved attribute-based encryption technique towards the data security in cloud computing. Concurr Comput Pract Exerc 1:5. Google Scholar
  3. 3.
    Namasudra S, Roy P, Balamurugan B (2017) Cloud computing: fundamentals and research issues. In: Proceedings of the 2nd international conference on recent trends and challenges in computational models, IEEE, Tindivanam, IndiaGoogle Scholar
  4. 4.
    Namasudra S, Nath S, Majumder A (2014) Profile based access control model in cloud computing environment. In: Proceedings of the international conference on green computing, communication and electrical engineering, IEEE, Coimbatore, India, pp. 1–5Google Scholar
  5. 5.
    Namasudra S, Roy P (2017) A new secure authentication scheme for cloud computing environment. Concurr Comput Pract Exerc 29(20):e3864. CrossRefGoogle Scholar
  6. 6.
    Namasudra S, Roy P (2016) Secure and efficient data access control in cloud computing environment: a survey. Multiagent Grid Syst Int J 12(2):69–90CrossRefGoogle Scholar
  7. 7.
    Namasudra S, Roy P (2017) Time saving protocol for data accessing in cloud computing. IET Commun 11(10):1558–1565CrossRefGoogle Scholar
  8. 8.
    Namasudra S, Roy P, Balamurugan B, Vijayakumar P (2017) Data accessing based on the popularity value for cloud computing. In: Proceedings of the international conference on innovations in information, embedded and communications systems (ICIIECS), IEEE, Coimbatore, IndiaGoogle Scholar
  9. 9.
    Namasudra S, Roy P (2015) Size based access control model in cloud computing. In: Proceedings of the international conference on electrical, electronics, signals, communication and optimization, IEEE, Visakhapatnam, India, pp. 1–4Google Scholar
  10. 10.
    Namasudra S, Roy P (2017) A new table based protocol for data accessing in cloud computing. J Inf Sci Eng 33(3):585–609MathSciNetGoogle Scholar
  11. 11.
    Namasudra S, Roy P (2018) PpBAC: popularity based access control model for cloud computing. J Org End User Comput 30(4):14–31CrossRefGoogle Scholar
  12. 12.
    Sarkar S, Saha K, Namasudra S, Roy P (2015) An efficient and time saving web service based android application. SSRG Int J Comput Sci Eng 2(8):18–21Google Scholar
  13. 13.
    Namasudra S (2018) Cloud computing: a new era. J Fundam Appl Sci 10(2):113–135Google Scholar
  14. 14.
    Li M, Yu S, Ren K, Lou W (2010) Securing personal health records in cloud computing: patient-centric and fine-grained data access control in multi-owner settings. In: Proceedings of the international conference on security and privacy in communication systems, pp. 89–106Google Scholar
  15. 15.
    Liu X, Lu R, Ma J, Chen L, Qin B (2016) Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J Biomed Health Inform 20(2):655–668CrossRefGoogle Scholar
  16. 16.
    Mathew G, Obradovic Z (2011) A privacy-preserving framework for distributed clinical decision support. In: Proceedings of the computational advances in bio and medical sciences, pp. 129–134Google Scholar
  17. 17.
    Polat K, Güneş S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Proc 17(4):694–701CrossRefGoogle Scholar
  18. 18.
    Sánchez AS, Iglesias-Rodríguez FJ, Fernández PR, Juez FJDC (2016) Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders. Int J Ind Ergon 52:92–99CrossRefGoogle Scholar
  19. 19.
    Huang F, Wang S, Chan CC (2012) Predicting disease by using data mining based on healthcare information system. In: Proceedings of the IEEE international conference on granular computing, pp. 191–194Google Scholar
  20. 20.
    Krishnaiah V, Narsimha DG, Chandra NS (2013) Diagnosis of lung cancer prediction system using data mining classification techniques. Int J Comput Sci Inf Technol 4(1):39–45Google Scholar
  21. 21.
    Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675–7680CrossRefGoogle Scholar
  22. 22.
    Bashir S, Qamar U, Khan FH (2015) BagMOOV: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas Phys Eng Sci Med 38(2):305–323CrossRefGoogle Scholar
  23. 23.
    Shilaskar S, Ghatol A (2013) Feature selection for medical diagnosis: evaluation for cardiovascular diseases. Expert Syst Appl 40(10):4146–4153CrossRefGoogle Scholar
  24. 24.
    Shao YE, Hou CD, Chiu CC (2014) Hybrid intelligent modeling schemes for heart disease classification. Appl Soft Comput 14(5):47–52CrossRefGoogle Scholar
  25. 25.
    Guan W, Gray A, Leyffer S (2009) Mixed-integer support vector machine. In: Proceedings of the NIPS workshop on optimization for machine learning, pp. 1–6Google Scholar
  26. 26.
    Hoa NS (1996) Some efficient algorithms for rough set methods. In: Proceedings IPMU’96 Granada, Spain, pp. 1541–1547Google Scholar
  27. 27.
    Ye D, Chen Z, Ma S (2013) A novel and better fitness evaluation for rough set based minimum attribute reduction problem. Inf Sci 222:413–423MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Yang XS (2009) Firefly algorithms for multimodal optimization. In: Proceedings of the 5th international conference on stochastic algorithms: foundations and applications. Springer, Berlin, pp. 169–178Google Scholar
  29. 29.
    Tsumoto S (2000) Problems with mining medical data. In: Proceedings of the 24th annual international computer software and applications conference, IEEE, Taipei, TaiwanGoogle Scholar
  30. 30.
    Neagoe VE, Iatan IF, Grunwald S (2003) A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis. In: The proceedings of the AMIA Annual Symposium, pp. 494–498Google Scholar
  31. 31.
    Ordonez C. (2004). Improving heart disease prediction using constrained association rules. In: Seminar presentation at University of TokyoGoogle Scholar
  32. 32.
    Noh K, Lee HG, Shon HS, Lee BJ, Ryu KH (2006) Associative classification approach for diagnosing cardiovascular disease (LNCIS, 345). Springer, Berlin, pp 721–727zbMATHGoogle Scholar
  33. 33.
    Koutsojannis C, Hatzilygeroudis I (2007) Using a neurofuzzy approach in medical application (LNCS, 4693). Springer, Berlin, pp 477–484Google Scholar
  34. 34.
    Tsipouras MG, Exarchos TP, Fotiadis DI, Kotsiam AP, Vakalis KV, Naka KK, Michalis LK (2008) Automated diagnosis of coronary artery disease based on data mining and Fuzzy modeling. IEEE Trans Inf Technol Biomed 12(4):447–458CrossRefGoogle Scholar
  35. 35.
    Vazirani H, Kala R, Shukla A, Tiwari R (2010) Use of modular neural network for heart disease. Int J Comput Commun Technol 1(2–4):88–93Google Scholar
  36. 36.
    Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J King Saud Univ Comput Inf Sci 24(1):27–40Google Scholar
  37. 37.
    Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447CrossRefGoogle Scholar
  38. 38.
    Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefGoogle Scholar
  39. 39.
    Bai Y, Han X, Chen T, Yu H (2015) Quadratic kernel-free least squares support vector machine for target diseases classification. J Comb Opt 30(4):850–870MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Sharawardi NSA, Choo YH, Chong SH, Muda AK, Goh OS (2014) Single channel sEMG muscle fatigue prediction: an implementation using least square support vector machine. In: Proceedings of the 4th world congress on information and communication technologies, IEEE, Bandar Hilir, Malaysia, pp. 320–325Google Scholar
  41. 41.
    Han J, Kamber M, Pei J (2011) Data mining concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, BurlingtonzbMATHGoogle Scholar
  42. 42.
    Singh YN, Gupta P (2007) Quantitative evaluation of normalization techniques of matching scores in multimodal biometric systems (LNCS, 4642). Springer, Berlin, pp 574–583Google Scholar
  43. 43.
    Brigham EO (1988) The fast Fourier transform and its applications. Prentice-Hall, Englewood CliffsGoogle Scholar
  44. 44.
    Alfred M (1999) Signal analysis wavelets, filter banks, time-frequency transforms and applications. Wiley, New YorkzbMATHGoogle Scholar
  45. 45.
    Li S, Tang B, He H (2016) An imbalanced learning based MDRTB early warning system. J Med Syst 40(7):1–9CrossRefGoogle Scholar
  46. 46.
    Gao H, Jian S, Peng Y, Liu X (2016) A subspace ensemble framework for classification with high dimensional missing data. Multidimens Syst Signal Process 28(4):1309–1324CrossRefGoogle Scholar
  47. 47.
    Lafta R, Zhang J, Tao X, Li Y, Tseng VS (2015) An intelligent recommender system based on short-term risk prediction for heart disease patients. In: Proceedings IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), IEEE, Singapore, Singapore, pp. 102–105Google Scholar
  48. 48.
    Lafta R, Zhang J, Tao X, Li Y, Tseng VS, Luo Y, Chen F (2016) An intelligent recommender system based on predictive analysis in telehealthcare environment. Web Intell 14(4):325–336CrossRefGoogle Scholar
  49. 49.
    Rizwan P, Rajsekhara Babu M, Suresh K (2017) Design and development of low investment smart hospital using internet of things through innovative approaches. Biomed Res 28(11):4979–4985Google Scholar
  50. 50.
    Rizwan P, Babu MR, Balamurugan B, Suresh K (2018) Real-time big data computing for internet of things and cyber physical system aided medical devices for better healthcare. In: Majan international conference (MIC), IEEE, pp. 1–8Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia

Personalised recommendations