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Comparative Analysis of Data Mining Classification Techniques for Prediction of Heart Disease Using the Weka and SPSS Modeler Tools

  • Atul Kumar RamotraEmail author
  • Amit Mahajan
  • Rakesh Kumar
  • Vibhakar Mansotra
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)

Abstract

The healthcare sector generates enormous data related to electronic medical records containing detailed reports, test, and medications. Research in the field of health care is being carried out to utilize the available healthcare data effectively using data mining. Every year, heart disease causes millions of deaths around the world. This research paper intends to analyze a few important parameters and utilize data mining classification techniques to predict the presence of heart disease. Data mining techniques are very useful in identifying the hidden patterns and information in the dataset. Decision Tree, Naïve Bayes, Support Vector Machines, and Artificial Neural Networks classifiers are used for the prediction in Weka and SPSS Modeler tools and comparison of results is done on the basis of sensitivity, specificity, precision, and accuracy. Naive Bayes classifier achieved the highest accuracy of 85.39% in the Weka tool, and in the SPSS Modeler tool, SVM classifier achieved the highest accuracy at 85.87%.

Keywords

Data mining Classification Heart disease Decision tree Naïve Bayes Support vector machines k-Nearest Neighbor Artificial neural networks Weka SPSS Modeler Sensitivity Selectivity Prediction Accuracy 

References

  1. 1.
    Liao, S.H., Chu, P.H., Hsiao, P.Y.: Data mining techniques and applications—a decade review from 2000 to 2011. Expert. Syst. Appl. 11303–11311 (2012)CrossRefGoogle Scholar
  2. 2.
    Koppad, S.H., Kumar, A.: Application of big data analytics in healthcare system to predict COPD. In: International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–5 (2016)Google Scholar
  3. 3.
    Singh, P., Mansotra, V.: Data mining based tools and techniques in public health care management: a study. In: 11th International Conference on Computing for Sustainable Global Development, India Com (2017)Google Scholar
  4. 4.
    Silwattananusarn, T., Tuamsuk, K.: Data mining and its applications for knowledge management: a literature review from 2007 to 2012. Int. J. Data Min. Knowl. Manag. Process. 13–24 (2012)CrossRefGoogle Scholar
  5. 5.
    World Health Organization: Non-communicable diseases. http://www.who.int/mediacentre/factsheets/fs355/en/. Accessed 18 June 2018
  6. 6.
    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, pp. 514–518 (2012)Google Scholar
  7. 7.
    Mastrogiannis, N., Boutsinas, B., Giannikos, I.: Methods for improving the accuracy of data mining classification algorithms. Comput. Oper. Res. 2829–2839 (2009)CrossRefGoogle Scholar
  8. 8.
    Arumugam, P., Christy, V.: Analysis of clustering and classification methods for actionable knowledge. Mater. Today Proc. 1839–1845 (2018)Google Scholar
  9. 9.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers (2006)Google Scholar
  10. 10.
    Aljaaf, A.J., Al-Jumeily, D., Hussain, A.J., Dawson, T., Fergus, P., Al-Jumaily, M.: Predicting the likelihood of heart failure with a multi level risk assessment using decision tree. In: Third International Conference on Technological Advances in Electrical, pp. 101–106. Beirut, Lebanon (2015)Google Scholar
  11. 11.
    Alexopoulos, E., Dounias, G., Vemmos, K.: Medical diagnosis of stroke using inductive machine learning. Mach. Learn. Appl. 20–23 (1999)Google Scholar
  12. 12.
    Jabbar, M.A., Samreen, S.: Heart disease prediction system based on hidden Naïve Bayes classifier. In: 2016 International Conference on Circuits, Controls, Communications and Computing, pp. 1–5 (2016)Google Scholar
  13. 13.
    Orphanou, K., Dagliati, A., Sacchi, L., Stassopoulou, A., Keravnou, E., Bellazzi, R.: Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis. J. Biomed. Inform. 74–82 (2018)Google Scholar
  14. 14.
    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, pp. 52–61 (2013)Google Scholar
  15. 15.
    Yang, G., Ren, Y., Pan, Q.: A heart failure diagnosis model based on support vector machine. In: 3rd International Conference on Biomedical Engineering and Informatics (2010)Google Scholar
  16. 16.
    Alty, S.R., Millasseau, S.C, Chowienczyk, P.J., Jakobsson, A.: Cardiovascular disease prediction using support vector machines. In: 46th Midwest Symposium on Circuits and Systems. IEEE J. Biomed. Health Inform. (2003)Google Scholar
  17. 17.
    Masetic, Z., Subasi, A.: A congestive heart failure detection using random forest classifier. Comput. Methods Prog. Biomed. 54–64 (2016)CrossRefGoogle Scholar
  18. 18.
    Lu, H., Setiono, R., Liu, H.: Effective data mining using neural networks. IEEE Trans. Knowl. Data Eng. 957–961 (1996)Google Scholar
  19. 19.
    Gharehchopogh, F.S., Khalifelu, Z.A.: Neural network application in diagnosis of patient: a case study. In: International Conference on Computer Networks and Information Technology, pp. 245–249, Abbottabad (2011)Google Scholar
  20. 20.
    Heart Attack Dataset from http://archive.ics.uci.edu/ml/datasets/HeartDisease. Accessed 11 Sept 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Atul Kumar Ramotra
    • 1
    Email author
  • Amit Mahajan
    • 1
  • Rakesh Kumar
    • 1
  • Vibhakar Mansotra
    • 1
  1. 1.Department of Computer Science & ITUniversity of JammuJammuIndia

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