Machine Learning Based Optimal Data Classification Model for Heart Disease Prediction

  • R. BhuvaneeswariEmail author
  • P. Sudhakar
  • R. P. Narmadha
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)


Heart disease (HD) is a greatest reason for high death rate among the number of inhabitants present on the planet. Identification of HD is viewed as a significant subject in the area of medical data examination. The measure of data in the medicinal field is tremendous. Data mining transforms the huge accumulation of actual medical data into useful data for making decisions. In this paper, prediction models were created by utilizing the ML technique called J48 classifier. In order to enhance the results further, a correlation based feature selection (CFS) model is applied to perform the feature selection process. Test results demonstrate that the HD prediction model shows excellent results over the compared methods.


CFS J48 Feature selection Prediction 


  1. 1.
    World Health Organization (WHO): Cardiovascular diseases (CVDs) – Key Facts (2017).
  2. 2.
    Srinivas, K., Rao, G.R., Govardhan, A.: Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques. In: Paper presented at the 5th International Conference on Computer Science and Education (ICCSE), Hefei, pp. 1344–1349 (2010)Google Scholar
  3. 3.
    Paul, A.K., Shill, P.C., Rabin, M.R.I., Akhand, M.A.H.: Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. (ICIEV). In: 5th International Conference on Informatics, Electronics and Vision, pp. 145–150. IEEE (2016)Google Scholar
  4. 4.
    Kavitha, R., Kannan, E.: An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining. In: International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pp. 1–5 (2016)Google Scholar
  5. 5.
    Shouman, M., Turner, T., Stocker, R.: Integrating clustering with different data mining techniques in the diagnosis of heart disease. J. Comput. Sci. Eng. 20 (1) (2013)Google Scholar
  6. 6.
    Dey, A., Singh, J., Singh, N.: Analysis of supervised machine learning algorithms for heart disease prediction with reduced number of attributes using principal component analysis. Analysis 140(2), 27–31 (2016)Google Scholar
  7. 7.
    Anooj, P.K.: Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J. King Saud Inf. Univ.-Comput. Sci. 24(1), 27–40 (2012)Google Scholar
  8. 8.
    Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., Wang, Q.: A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput. Math. Methods Med. 2017, 11 (2017)MathSciNetGoogle Scholar
  9. 9.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • R. Bhuvaneeswari
    • 1
    Email author
  • P. Sudhakar
    • 1
  • R. P. Narmadha
    • 2
  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChidambaramIndia
  2. 2.Department of CSESri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia

Personalised recommendations