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Predictive Analytics for Cardiovascular Disease Diagnosis Using Machine Learning Techniques

  • Anandhavalli MuniasamyEmail author
  • Vasanthi Muniasamy
  • Roheet Bhatnagar
Conference paper
  • 70 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

Medical data can be mined for effective decision making in the presence of disease analysis. Globally, cardiovascular alias heart disease is one of the highly rated causes of death [1] disease which will lead to 76% of the deaths [2体育赛事投注记录] by the year 2030. Currently, the techniques of machine learning and predictive analytics have proven importance in medical data analysis. In a nutshell, this paper aims to apply six classifiers namely artificial neural network, support vector machine, decision tree, nearest neighbor, linear discriminant analysis, random forest, and to predict the presence of heart diseases in the patient’s datasets. Moreover, the performance of these classifiers on three heart disease datasets is compared. The results reveal that RF, LDA, DT, and ANN have performed better than SVM and KNN in terms of accuracy, recall, F1-score, confusion matrix, and error rate.

Keywords

Predictive analytics Healthcare Machine learning Artificial neural network (ANN) Support vector machine (SVM) Decision tree (DT) Nearest neighbor (NN) Linear discriminant analysis (LDA) Random forest (RF) 

Notes

Acknowledgements

体育赛事投注记录we acknowledge king khalid university for providing the opportunity to initiate this paperwork and uci data repository for datasets.

References

  1. 1.
    World Health Organization (WHO). )
  2. 2.
    World Health Organization: Prevention of diabetes mellitus-report of a WHO study group. WHO Technical Report Series no. 844, World Health Organization, Geneva (1994).
  3. 3.
    Agarwal, R., Dhar, V.: Big data, data science, and analytics: the opportunity and challenge for is research. Inf. Syst. Res. 25(3), 443–448 (2014)
  4. 4.
    Heart Disease and Stroke Statistics-2019.
  5. 5.
    Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 4(1), 1–37 (2012)
  6. 6.
    Alizadehsani, R., Habibi, J., Hosseini, M.J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Bahadorian, B., Sani, Z.A.: A data mining approach for diagnosis of coronary artery disease. Comput. Methods Programs Biomed. 111(1), 52–61 (2013)
  7. 7.
    Verma, L., Srivastaa, S., Negi, P.C.: A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J. Med. Syst. 40(178) (2016).  
  8. 8.
    Nithya, B.: Study on predictive analytics practices in health care system. IJETTCS 5 (2016)
  9. 9.
    Chowdhury, D.R., Chatterjee, M., Samanta, R.K.: An artificial neural network model for neonatal disease diagnosis. Int. J. Artif. Intell. Expert Syst. (IJAE) 2(3) (2011)
  10. 10.
    Vanisree, K., Singaraju, J.: Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. Int. J. Comput. Appl. (0975 – 8887) 19(6) (2011)
  11. 11.
    Srinivas, K., Rani, B.K., Govrdhan, A.: Applications of data mining techniques in healthcare and prediction of heart attacks. Int. J. Comput. Sci. Eng. (IJCSE) 2(2), 250–255 (2010)
  12. 12.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
  13. 13.
    Burges C.: A Tutorial on Support Vector Machines for Pattern Recognition. Bell Laboratories and Lucent Technologies (1998)
  14. 14.
    Burbidge, R., Buxton, B.: An introduction to Support Vector Machines for Data Mining. Computer Science Departmentm, UCL (2001)
  15. 15.
    Bardhan, I., Oh, J., Zheng, Z., Kirksey, K.: Predictive analytics for readmission of patients with congestive heart failure. Inf. Syst. Res. 26(1), 19–39 (2014)
  16. 16.
    Singh, Y., Sinha, N., Sanjay, S.: Heart disease prediction system using random forest, pp. 613–623 (2017).  
  17. 17.
    Fawagreh, K., Gaber, M.M., Elya, E.: RF.: From early developments to recent advancements. Syst. Sci. Control Eng. 2(1), 602–609 (2014)
  18. 18.
    Pang, S., Ozawa, S., Kasabov, N.: Incremental linear discriminant analysis for classification of data streams. IEEE Trans. Syst. Man Cybern. B. 35(5), 905–914 (2006)
  19. 19.
    Rossouw, J.E., du Plessis, J., Benade, A., Jordaan, P., Kotze, J., Jooste, P.: Coronary risk factor screening in three rural communities. S. Afr. Med. J. 64, 430–436 (1983)
  20. 20.
    Li, T., Zhu, S., Ogihara, M.: Using discriminant analysis for multi-class classification: an experimental investigation. Knowl. Inf. Syst. 10(4), 453–472 (2006)
  21. 21.
    Cako, S., Njeguš, A., Matić, V.: Effective diagnosis of heart disease presence using artificial neural networks. In: International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, pp. 3–8 (2017)
  22. 22.
    Hongjun, Lu: Setiono R, Huan Liu: Effective data mining using neural networks. IEEE Trans. Knowl. Data Eng. 8(6), 957–961 (1996)
  23. 23.
    Kamruzzaman, Sarkar A.: A new data mining scheme using artificial neural networks. Sensors 11(12), 4622–4647 (2011)
  24. 24.
    Kumari, M., Godara, S.: Comparative study of data mining classification methods in cardiovascular disease prediction. IJCST 2(2) (2011)
  25. 25.
    Singh, Y., Chauhan, A.S.: Neural network in data mining. J. Theor. Appl. Inf. Technol. 37–42 (2009)
  26. 26.
    El-Bialy, R., Salamay, M.A., Karam, O.H., Khalifa, M.E.: Feature analysis of coronary artery heart disease data sets. Procedia Comput. Sci. ICCMIT 265, 459–468 (2015).  
  27. 27.
    Verma, L., Srivastaa, S., Negi, P.C.: A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J. Med. Syst. 40(178) (2016)
  28. 28.
    Alizadehsani, R., Hosseini, M.J., Sani, Z.A., Gandeharioun, A., Boghrati, R.: Diagnosis of coronary artery disease using cost-sensitive algorithms. In: IEEE 12th International Conference on Data Mining Workshop, pp. 9–16 (2012)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Anandhavalli Muniasamy
    • 1
    Email author
  • Vasanthi Muniasamy
    • 1
  • Roheet Bhatnagar
    • 2
  1. 1.College of Computer ScienceKing Khalid UniversityAbhaKingdom of Saudi Arabia
  2. 2.Department of CSEManipal University JaipurJaipurIndia

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