Prediction of Occurrence of Heart Disease and Its Dependability on RCT Using Data Mining Techniques

  • Pinky Bajaj
  • Kavita Choudhary
  • Renu Chauhan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)


This paper is basically extension of our work in detection of diseases using data mining techniques. Here it shows that heart disease can be diagnose by using various data mining techniques and algorithms such as decision tree, split validation and apply model. Some Attributes like age, root canal treatment, smoking and diabetes predicts the probability of patients getting a heart disease. The proposed system that is the union of computer-based patient records with clinical decision support can lower down medical mistakes, unwanted practice variation and helps in improving patient safety and outcome.


Validation algorithm Decision tree Apply model Age Smoking Diabetes Max heart rate RCT Diet 


  1. 1.
    Peter, T.J., Somasundaram, K.: An empirical study on prediction of heart disease using classification data mining techniques. In: IEEE International Conference on Advances in Engineering, Science and Management (ICAESM-2012), pp. 514–518 (2012)Google Scholar
  2. 2.
    Durairaj, M., Ranjani, V.: Data mining applications in healthcare sector a study. Int. J. Sci. Technol. Res. IJSTR 2(10) (2013)Google Scholar
  3. 3.
    Vijiyarani, S., Sudha, S.: Disease prediction in data mining technique—a survey. Int. J. Comput. Appl. Inf. Technol. II(I) (ISSN: 2278-7720) (2013)Google Scholar
  4. 4.
    Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: IEEE/ACS International Conference on Computer Systems and Applications, 2008, AICCSA 2008, pp. 108–115 (2008)Google Scholar
  5. 5.
    Collste, G., Duquenoy, P., George, C., Hedstrom, K., Kimppa, K., Mordini, E.: ICT in medicine and health care: assessing social, ethical and legal issues.
  6. 6.
    Chandna, D.: Diagnosis of heart disease using data mining algorithm. Int. J. Comput. Sci. Inf. Technol. (IJCSIT), 5(2), 1678–1680 (2014)Google Scholar
  7. 7.
    Wilson, A., Wilson, G., Joy L.K.: Heart disease prediction using the data mining techniques. Int. J. Comput. Sci. Trends Technol. (IJCST) 2(1) (2014)Google Scholar
  8. 8.
    Shukla, D.P., Patel, S.B, Sen, A.K.: A literature review in health informatics using data mining techniques. Int. J. Softw. Hardware Res. Eng. IJOURNALS (2014)Google Scholar
  9. 9.
    Subbalakshmi, G., Ramesh, K., Chinna Rao, M.: Decision support in heart disease prediction system using naive bayes. Ind. J. Comput. Sci. Eng. (IJCSE) 2(2), 0976–5166 (2011)Google Scholar
  10. 10.
    Rudowski, R.: Impact of information and communication technologies (ICT) on HealthCare.
  11. 11.
    Pandey, A.K., Pandey, P., Jaiswal, K.L., Sen, A.K..: Data mining clustering techniques in the prediction of heart disease using attribute selection method. Int. J. Sci. Eng. Technol. Res. (IJSETR) 2(10), (2013)Google Scholar
  12. 12.
    Sayad, A.T., Halkarnikar, P.P.: Diagnosis of heart disease using neural network approach. In: Proceedings of IRF International Conference, 13th April-2014, Pune, India, ISBN: 978-93-84209-04-9 (2014)Google Scholar
  13. 13.
    Shouman, M., Turner, T., Stocker, R.: Using decision tree for diagnosing heart disease patients. In: Proceedings of the 9-th Australasian Data Mining Conference (AusDM’11), Ballarat, Australia (2011)Google Scholar
  14. 14.

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.ITM UniversityGurgaonIndia

Personalised recommendations