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Heart Disorder Prognosis Employing KNN, ANN, ID3 and SVM

  • Jyoti Deshmukh
  • Mukul JangidEmail author
  • Shreeshail Gupte
  • Siddhartha Ghosh
Conference paper
  • 61 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

cardiovascular disease (cvd) stands as a type of disease that incorporates the heart or the blood vessels. over the past few decades, it has become the common result of a death in underdeveloped, developing as well as in developed countries. this field is thus, “data rich” but “knowledge poor”. here, data mining holds great potential and immensely helps the health system to systematically use the data and analytics to recognize the inefficiencies thus, reducing the practice of burdensome tests. author presents the heart disorder prognosis system for accurate detection of heart disease which has been derived from distinctive analysis among several data mining algorithms. the presence of heart disorder in a sufferer is forecasted by digging out appealing patterns from the datasets. the datasets used for analysis are fetched from the uci machine learning repository, namely cleveland clinical foundations and the hungarian institute of cardiology. this paper tries to introduce the methodology, implementation and analysis of decision tree (id3), support vector machine (svm), artificial neural network (ann) and k-nearest neighbor (knn) algorithm for detection of heart diseases. the conclusion is induced on the basis of accuracy and roc value. id3 algorithm gives better performance over other algorithms for both the datasets.

Keywords

Classifiers Heart disease Prediction Sensitivity Specificity 

Notes

Acknowledgement

the authors intend to convey gratefulness to dr. andras janosi, m.d. and dr. robert detrano, m.d., ph.d., for permitting us to use the heart disease data set for our research work.

References

  1. 1.
    Chadha, R., Mayank, S.: Prediction of heart disease using data mining techniques. CSI Trans. ICT 4(2–4), 193–198 (2016)
  2. 2.
    Krishnaiah, V., Narsimha, G., Chandra, N.S.: Heart disease prediction system using data mining technique by fuzzy KNN approach. In: Emerging ICT for Bridging the Future- Proceedings of the 49th Annual Convention of the Computer Society of India (CSI), vol. 1, pp. 371–384. Springer, Cham (2015)
  3. 3.
    Gandhi, M., Singh, S.N.: Predictions in heart disease using techniques of data mining. In: Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), International Conference, pp. 520–525. IEEE (2015).
  4. 4.
    Bhatia, S., Prakash, P., Pillai, G.N.: SVM based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features. In: Proceedings of the World Congress on Engineering and Computer Science, pp. 34–38 (2008)
  5. 5.
    Deepika, M., Kalaiselvi, K.: A Empirical study on disease diagnosis using data mining techniques. In: Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 615–620. IEEE (2018)
  6. 6.
    Gnaneswar, B., Jebarani, M.E.: A review on prediction and diagnosis of heart failure. In: Innovations in Information, Embedded & Communication Systems (ICIIECS), International Conference, pp.1–3. IEEE (2017)
  7. 7.
    Karimifard, S., Ahmadian, A., Khoshnevisan, M., Nambakhsh, M.S.: Morphological heart arrhythmia detection using hermitian basis functions and KNN classifier. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1367–1370. IEEE (2006).
  8. 8.
    Jabbar, M.A., Deekshatulu, B.L., Chandra, P.: Classification of heart disease using artificial neural network and feature subset selection. Global J. Comput. Sci. Technol. Neural Artif. Intell. 13(3) (2013).
  9. 9.
    Saglain, M., Hussain, W., Saqib, N.A., Khan, M.A.: Identification of heart failure by using unstructured data of cardiac patients. In: 2016 45th International Conference on Parallel Processing Workshops (ICPPW), pp. 426–43. IEEE (2016)
  10. 10.
    Avci, E.: A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier. Expert Syst. Appl. 36(7), 10618–10626 (2009)
  11. 11.
    Mane, T.U.: Smart heart disease prediction system using Improved K-means and ID3 on big data. In: 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 239–245. IEEE (2017)
  12. 12.
    Maglogiannis, I., Loukis, E., Zafiropoulos, E., Stasis, A.: Support vectors machine-based identification of heart valve diseases using heart sounds. Comput. Methods Programs Biomed. 95(1), 47–61 (2009)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Jyoti Deshmukh
    • 1
  • Mukul Jangid
    • 1
    Email author
  • Shreeshail Gupte
    • 1
  • Siddhartha Ghosh
    • 1
  1. 1.Department of Computer EngineeringRajiv Gandhi Institute of TechnologyMumbaiIndia

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