Advertisement

A Review on Predicting Cardiovascular Diseases Using Data Mining Techniques

  • V. PavithraEmail author
  • V. Jayalakshmi
Conference paper
  • 36 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

The main objective of the work is to analyze various data mining techniques in the health care field that can be employed in “predicting cardiovascular diseases and their efficient diagnosis. Cardiovascular system is the first organ system to become fully functional in uterus. Cardiovascular diseases is one of the major common diseases that cause death all around worldwide. People of all ages are affected by this disease particularly the elderly people. It is essential to predict the group of people commonly affected and identifying risk factors like age, sex, lifestyle that will be helpful in early diagnosing and prevention of heart diseases. At present huge number of people are pretentious to heart diseases and hence it is quite difficult to predict accurately. Proper mining methods can save enormous number of people from mortality due to heart diseases. This paper analyses various types of heart disease and prediction techniques used in heart disease prediction.

Keywords

Data mining Cardiovascular diseases Data mining techniques Data mining tool 

References

  1. 1.
    Khan, Y., Qamar, U., Yousaf, N., Khan, A.: Machine learning techniques for heart disease datasets: a survey. In: Proceedings of the 2019 11th International Conference on Machine Learning and Computing, pp. 27–35. ACM (2019)Google Scholar
  2. 2.
    Verma, C.V., Ghosh, S.M.: Review of cardiovascular disease in diabetic patients using data mining techniques (2017)Google Scholar
  3. 3.
    Ansari, H.F., Namdeo, V.: An efficient SKNN based approach for heart disease classification. Int. J. Adv. Technol. Eng. Explor. 6(53), 101–106 (2019)CrossRefGoogle Scholar
  4. 4.
    Raman, M., Sharma, V.K.: Classification utility & procedures for recognition of heart disease: a review. Int. J. Sci. Res. Sci. Technol. (IJSRST) 3(8), 383–387 (2017)Google Scholar
  5. 5.
    Musunuru, K., Kathiresan, S.: Genetics of common, complex coronary artery disease. Cell 177(1), 132–145 (2019)CrossRefGoogle Scholar
  6. 6.
    Guo, J., Erqou, S.A., Miller, R.G., Edmundowicz, D., Orchard, T.J., Costacou, T.: The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes. Diabetologia 62(2), 259–268 (2019)CrossRefGoogle Scholar
  7. 7.
    Michael, F.G., Mann, D.L.: Heart Failure: A Companion to Braunwald’s Heart Disease E-Book. Elsevier Health Sciences, Berlin (2019)Google Scholar
  8. 8.
    Tarawneh, M., Embarak, O.: Hybrid approach for heart disease prediction using data mining techniques. In: International Conference on Emerging Internetworking, Data & Web Technologies, pp. 447–454. Springer, Cham (2019)Google Scholar
  9. 9.
    Bashir, S., Khan, Z.S., Khan, F.H., Anjum, A., Bashir, K.: Improving heart disease prediction using feature selection approaches. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 619–623. IEEE (2019)Google Scholar
  10. 10.
    Manogaran, G., Varatharajan, R., Priyan, M.K.: Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools Appl. 77(4), 4379–4399 (2018)CrossRefGoogle Scholar
  11. 11.
    Ambekar, S., Phalnikar, R.: Disease risk prediction by using convolutional neural network. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. IEEE (2018)Google Scholar
  12. 12.
    Chavda, P., Bhavsar, H., Pithadia, Y., Kotecha, R.: Early detection of cardiac disease using machine learning. Available at SSRN 3370813 (2019)Google Scholar
  13. 13.
    Maji, S., Arora, S.: Decision tree algorithms for prediction of heart disease. In: Information and Communication Technology for Competitive Strategies, pp. 447–454. Springer, Singapore (2019)Google Scholar
  14. 14.
    Joseph, S.I.T.: Survey of data mining algorithm’s for intelligent computing system. J. Trends Comput. Sci. Smart Technol. (TCSST) 1(01), 14–24 (2019)CrossRefGoogle Scholar
  15. 15.
    Jin, B., Che, C., Liu, Z., Zhang, S., Yin, X., Wei, X.: Predicting the risk of heart failure with EHR sequential data modeling. IEEE Access 6, 9256–9261 (2018)CrossRefGoogle Scholar
  16. 16.
    Wiharto, W., Kusnanto, H., Herianto, H.: Hybrid system of tiered multivariate analysis and artificial neural network for coronary heart disease diagnosis. Int. J. Electr. Comput. Eng. 7(2), 1023 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computing SciencesVels Institute of Science, Technology and Advanced Studies (VISTAS)ChennaiIndia

Personalised recommendations