A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

Abstract

The healthcare domain is basically “data rich”, yet tragically not every one of the information are dug which is required for finding concealed examples and successful basic leadership used to find learning in database and for restorative research, especially in heart malady forecast. This article has examined forecast frameworks for heart disease utilizing more number of info attributes. In this article, we proposed an altered calculation for classification with decision trees which furnishes precise outcomes when contrasted and others calculations. The proposed work is planned to show the data mining method in disease forecast frameworks in medicinal space by utilizing avaricious way to deal with select the best attributes. Our investigation demonstrates that among various prediction models neural networks and Gini index prediction models results with most noteworthy precision for heart attack prediction. A portion of the discretization strategies like voting technique are known to deliver more precise decision trees. To improve execution in coronary illness finding, this research work examines the outcomes in the wake of applying a scope of procedures to various sorts of decision trees and accuracy and sensitivity are attained by the execution of elective decision tree methods.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. 1.

    Sen K, Patel SB, Shukla DP (2013) A data mining technique for prediction of coronary heart disease using neuro-fuzzy integrated approach two level. Int J Eng Comput Sci 2(9):1663–1671

    Google Scholar 

  2. 2.

    Ishtake S, Sanap S (2013) Intelligent heart disease prediction system using data mining techniques. Int J Healthc Biomed Res 1(3):94–101

    Google Scholar 

  3. 3.

    Chaurasia V (2013) Early prediction of heart diseases using data mining. Caribb J Sci Technol 1:208–217

    Google Scholar 

  4. 4.

    Chaitrali S, Sulabha AS (2012) A data mining approach for prediction of heart disease using neural networks. Int J Comput Eng Technol 3(3):30–40

    Google Scholar 

  5. 5.

    Lopez D, Manogaran G, Jagan J (2017) Modelling the H1N1 influenza using mathematical and neural network approaches. Biomed Res 28(8):1–5

    Google Scholar 

  6. 6.

    Manogaran G, Lopez D (2017) Disease surveillance system for big climate data processing and dengue transmission. Int J Ambient Comput Intell (IJACI) 8(2):88–105

    Article  Google Scholar 

  7. 7.

    Manogaran G, Lopez D (2017) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng

  8. 8.

    Manogaran G, Lopez D (2017) A Gaussian process based big data processing framework in cluster computing environment. Clust Comput 1–16

  9. 9.

    Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2017) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust Comput 1–10

  10. 10.

    Jabbar M, Chandra P, Deekshatulu B (2011) Cluster based association rule mining for heart attack prediction. J Theor Appl Inf Technol 32(2):196–201

    Google Scholar 

  11. 11.

    Rao R (2011) Survey on prediction of heart morbidity using data mining techniques. Int J Data Min Knowl Manag Process (IJDKP) 1(3):14–34

    Article  Google Scholar 

  12. 12.

    Vijiyarani S, Sudha S (2013) Disease prediction in data mining technique—a survey. Int J Comput Appl Inf Technol II(I):17–21

    Google Scholar 

  13. 13.

    Yanwei X, Wang J, Zhao Z, Gao Y (2007) Combination data mining models with new medical data to predict outcome of coronary heart disease. In: Proceedings international conference on convergence information technology, pp 868–872

  14. 14.

    Varatharajan R, Manogaran G, Priyan MK (2017) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl 1–21

  15. 15.

    Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting. Future Gener Comput Syst

  16. 16.

    Manogaran G, Vijayakumar V, Varatharajan R, Kumar PM, Sundarasekar R, Hsu CH (2017) Machine learning based big data processing framework for cancer diagnosis using hidden markov model and GM clustering. Wirel Pers Commun 1–18

  17. 17.

    Palaniappan S, Awang R (2008) Intelligent heart disease prediction system using data mining techniques. Int J Comput Sci Netw Secur. https://doi.org/10.1109/AICCSA.2008.4493524

    Google Scholar 

  18. 18.

    Guru N, Dahiya A, Rajpal N (2007) Decision support system for heart disease diagnosis using neural network. Delhi Bus Rev 8(1):99–101

    Google Scholar 

  19. 19.

    Lee HG, Noh KY, Ryu KH (2007) Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. In: LNAI 4819: emerging technologies in knowledge discovery and data mining, pp 56–66

  20. 20.

    Patil SB, Kumaraswamy YS (2009) Intelligent and effective heart attack prediction system using data mining and artificial neural network. Eur J Sci Res 31(4):642–656

    Google Scholar 

  21. 21.

    Parthasarathy P, Vivekanandan S (2018) A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Inform Med Unlocked. https://doi.org/10.1016/j.imu.2018.03.001

    Google Scholar 

  22. 22.

    Noh K, Lee HG, Shon H-S, Lee BJ, Ryu KH (2006) Associative classification approach for diagnosing cardiovascular disease. In: Huang DS, Li K, Irwin GW (eds) Intelligent computing in signal processing and pattern recognition, vol 345. Springer, Berlin, pp 721–727

    Google Scholar 

  23. 23.

    Le Duff F, Munteanb C, Cuggiaa M, Mabob P (2004) Predicting survival causes after out of hospital cardiac arrest using data mining method. Stud Health Technol Inform 107(Pt 2):1256–1259

    Google Scholar 

  24. 24.

    Parthiban L, Subramanian R (2008) Intelligent heart disease prediction system using CANFIS and genetic algorithm. Int J Biol Biomed Med Sci 3(3):157–160

    Google Scholar 

  25. 25.

    Lopez D, Sekaran G (2016) Climate change and disease dynamics—a big data perspective. Int J Infect Dis 45:23–24

    Article  Google Scholar 

  26. 26.

    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, pp 141–154. IGI Global

  27. 27.

    Dangare CS, Apte SS (2012) Improved study of heart disease prediction system using data mining classification techniques. Int J Comput Appl 47(10):44–48

    Google Scholar 

  28. 28.

    Soni J, Ansari U, Sharma D, Soni S (2011) Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int J Comput Appl 17(8):43–48

    Google Scholar 

  29. 29.

    Pattekari SA, Parveen A (2012) Prediction system for heart disease using Naive Bayes. Int J Adv Comput Math Sci 3(3):290–294

    Google Scholar 

  30. 30.

    Manogaran G, Lopez D (2017) A survey of big data architectures and machine learning algorithms in healthcare. Int J Biomed Eng Technol 25(2–4):182–211

    Article  Google Scholar 

  31. 31.

    Manogaran G, Varatharajan R, Priyan MK (2017) Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed Tools Appl 1–21

  32. 32.

    Aditya Sundar N, Pushpa Latha P, Rama Chandra M (2017) Performance analysis of classification data mining techniques over heart disease data base. Int J Eng Sci Adv Technol 2(3):470–478

    Google Scholar 

  33. 33.

    Thanigaivel R, Ramesh Kumar K (2015) Review on heart disease prediction system using data mining techniques. Asian J Comput Sci Technol 3(1):68–74

    Google Scholar 

  34. 34.

    López MI, Luna JM, Romero C, Ventura S (2012) Classification via clustering for predicting final marks based on student participation in forums. In: Proceedings of the 5th international conference on educational data mining

  35. 35.

    Anbarasi M, Anupriya E et al (2010) Enhanced prediction of heart disease with feature subset selection using genetic algorithm. Int J Eng Sci Technol 2(10):5370–5376

    Google Scholar 

  36. 36.

    Andreeva P (2006) Data modelling and specific rule generation via data mining techniques. In: International conference on computer systems and technologies—CompSysTech. Australian Bureau of Statistics, 2010. Retrieved 7 Feb 2011

  37. 37.

    Thuraisingham B (2000) A primer for understanding and applying data mining. IT Prof IEEE 2:28–31

    Article  Google Scholar 

  38. 38.

    Tu MC, D Shin et al (2009) Effective diagnosis of heart disease through bagging approach. In: Proceedings of the 2nd international conference on biomedical engineering and informatics. IEEE

  39. 39.

    Fayyad UM, Keki BI (1992) On the handing of continuous-valued attributes in decision tree generation. Mach Learn 8:87–102

    MATH  Google Scholar 

  40. 40.

    Kerber R (1992) ChiMerge: discretization of numeric attributes. In: Proceedings of the tenth national conference on artificial intelligence

  41. 41.

    Chandra I, Siva Kumar N, Gokulnath CB, Parthasarathy P (2018) IOT based fall detection and ambient assisted for elderly. Clust Comput. https://doi.org/10.1007/s10586-018-2329-2

    Google Scholar 

  42. 42.

    Data mining—applications and trends. http://www.tutorialspoint.com/data_mining/dm_applications_rends.htm. Accessed 24 Jan 2018

  43. 43.

    Vijendra S (2011) Efficient clustering for high dimensional data: subspace based clustering and density based clustering. Inf Technol J 10(6):1092–1105

    Article  Google Scholar 

  44. 44.

    Breiman D, Friedman L, Olshen JH, Stone CJ (1984) Classification and regression trees. The Wadsworth statistics/probability series. Wadsworth International Group, Belmont

    Google Scholar 

  45. 45.

    Dougherty J, Kohavi R et al (1995) Supervised and unsupervised discretization of continuous features. In: Proceedings of the 12th international conference on machine learning. Morgan Kaufmann, San Francisco, pp 194–202

  46. 46.

    Kotsiantis S, Kanellopoulos D (2006) Discretization techniques: a recent survey. Int Trans Comput Sci Eng 32(1):47–58

    Google Scholar 

  47. 47.

    Han J, Kamber M (2006) Data mining concepts and techniques. Morgan Kaufmann, Los Altos

    Google Scholar 

  48. 48.

    Singh Y, Chauhan AS (2005–2009) Neural networks in data mining. J Theor Appl Inf Technol 5:6

  49. 49.

    Frawley WJ, Piatetsky-Shapiro G (1996) Knowledge discovery in databases: an overview. AAAI Press, Menlo Park

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Parthasarathy Panchatcharam.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mathan, K., Kumar, P.M., Panchatcharam, P. et al. A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Des Autom Embed Syst 22, 225–242 (2018). https://doi.org/10.1007/s10617-018-9205-4

Download citation

Keywords

  • Decision tree
  • Gain ratio
  • Gini index
  • Classification methods
  • Neural classifier