A Review on Heart Disease Detection Using Machine Learning

Authors

  • Neha Madame  Computer Science, Bhabha Engineering Research Institute, Bhabha University, Bhopal, Madhya Pradesh, India
  • Prof. Mashhood Siddiqui  Computer Science, Bhabha Engineering Research Institute, Bhabha University, Bhopal, Madhya Pradesh, India

Keywords:

Data mining, Machine learning, Neural Network

Abstract

The successful application of data mining in highly visible fields like e-business, marketing, and retail has led to its application in other industries and sectors. Among these sectors just discovered is healthcare. The healthcare environment is still „information rich? but „knowledge poor?. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today?s medical research, particularly in Heart Disease Prediction. Number of experiments has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that the Decision Tree outperforms and sometime Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying a genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.

References

  1. M. Durairaj and V. Revathi, ``Prediction of heart disease using back propagationMLPalgorithm,'' Int. J. Sci. Technol. Res., vol. 4, no. 8, pp. 235_239, 2015.
  2.  A. Gavhane, G. Kokkula, I. Pandya, and K. Devadkar, ``Prediction of heart disease using machine learning,'' in Proc. 2nd Int. Conf. Electron.Commun. Aerosp. Technol. (ICECA), Mar. 2018, pp. 1275_1278.
  3. L. Baccour, ``Amended fused TOPSIS-VIKOR for classi_cation (ATOVIC) applied to some UCI data sets,'' Expert Syst. Appl., vol. 99,pp. 115_125, Jun. 2018. doi: 10.1016/j.eswa.2018.01.025.
  4.  P. S. Kumar, D. Anand, V. U. Kumar, D. Bhattacharyya, and T.-H. Kim, ``A computational intelligence method for effective diagnosis of heart
  5. disease using genetic algorithm,'' Int. J. Bio-Sci. BioATOMICol., vol. 8, no. 2, pp. 363_372, 2016.
  6.  F. Dammak, L. Baccour, and A. M. Alimi, ``The impact of criterion weights techniques in TOPSIS method of multi-criteria decision making in  crisp and intuitionistic fuzzy domains,'' in Proc. IEEE Int. Conf. Fuzzy Syst.(FUZZ-IEEE), vol. 9, Aug. 2015, pp. 1_8.
  7.  M. J. Liberatore and R. L. Nydick, ``The analytic hierarchy process in medical and health care decision making:Aliterature review,'' Eur. J. Oper.Res., vol. 189, no. 1, pp. 194_207, 2008.
  8. A. L. Bui, T. B. Horwich, and G. C. Fonarow, ``Epidemiology and risk pro_le of heart failure,'' Nature Rev. Cardiol., vol. 8, no. 1, p. 30, 2011.
  9. M. Durairaj and N. Ramasamy, ``A comparison of the perceptive approaches for preprocessing the data set for predicting fertility successrate,'' Int. J. Control Theory Appl., vol. 9, no. 27, pp. 255_260, 2016.
  10. L. A. Allen, L.W. Stevenson, K. L. Grady, N. E. Goldstein, D. D. Matlock, R. M. Arnold, N. R. Cook, G. M. Felker, G. S. Francis, P. J. Hauptman, E. P. Havranek, H. M. Krumholz, D. Mancini, B. Riegel, and J. A. Spertus, ``Decision making in advanced heart failure: A scienti_c statement from the American heart association,'' Circulation, vol. 125, no. 15,pp. 1928_1952, 2012.
  11.  S. Ghwanmeh, A. Mohammad, and A. Al-Ibrahim, ``Innovative arti_cial neural networks-based decision support system for heart diseases diagnosis,'' J. Intell. Learn. Syst. Appl., vol. 5, no. 3, 2013, Art. no. 35396.
  12.  Q. K. Al-Shayea, ``Arti_cial neural networks in medical diagnosis,'' Int. J. Comput. Sci. Issues, vol. 8, no. 2, pp. 150_154, 2011. [6] J. Lopez-Sendon, ``The heart failure epidemic,'' Medicographia, vol. 33, no. 4, pp. 363_369, 2011.
  13.  P. A. Heidenreich, J. G. Trogdon, O. A. Khavjou, J. Butler, K. Dracup, M. D. Ezekowitz, E. A. Finkelstein, Y. Hong, S. C. Johnston, A. Khera,D. M. Lloyd-Jones, S. A. Nelson, G. Nichol, D. Orenstein, P.W. F.Wilson, and Y. J. Woo, ``Forecasting the future of cardiovascular disease in theunited states: A policy statement from the American heart association,'' Circulation, vol. 123, no. 8, pp. 933_944, 2011.
  14. A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramig, ``Nonlinear speech analysis algorithms mapped to a standard metric achieve clinicallyuseful quanti_cation of average Parkinson's disease symptom severity,'' J. Roy. Soc. Interface, vol. 8, no. 59, pp. 842_855, 2011.107580 VOLUME 8, 2020 J. P. Li et al.: HD Identification Method Using ML Classification in E-Healthcare
  15. S. I. Ansarullah and P. Kumar, ``A systematic literature review on cardiovascular disorder identi_cation using knowledge mining andmachine learning method,'' Int. J. Recent Technol. Eng., vol. 7, no. 6S, pp. 1009_1015, 2019.
  16. S. Nazir, S. Shahzad, S. Mahfooz, and M. Nazir, ``Fuzzy logic based decision support system for component security evaluation,'' Int. Arab J. Inf. Technol., vol. 15, no. 2, pp. 224_231, 2018.
  17.  R. Detrano, A. Janosi,W. Steinbrunn, M. P_sterer, J.-J. Schmid, S. Sandhu, K. H. Guppy, S. Lee, and V. Froelicher, ``International application of a new probability algorithm for the diagnosis of coronary artery disease,'' Amer. J. Cardiol., vol. 64, no. 5, pp. 304_310, Aug. 1989.
  18.  S. Mohan, C. Thirumalai and G. Srivastava, "Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques," in IEEE Access, vol. 7, pp. 81542-81554, 2019, doi: 10.1109/ACCESS.2019.2923707.
  19. J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan and A. Saboor, "Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare," in IEEE Access, vol. 8, pp. 107562-107582, 2020, doi: 10.1109/ACCESS.2020.3001149.
  20. A. Abdellatif, H. Abdellatef, J. Kanesan, C. -O. Chow, J. H. Chuah and H. M. Gheni, "An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods," in IEEE Access, vol. 10, pp. 79974-79985, 2022, doi: 10.1109/ACCESS.2022.3191669.
  21. J. Yu, S. Park, S. -H. Kwon, K. -H. Cho and H. Lee, "AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals," in IEEE Access, vol. 10, pp. 43623-43638, 2022, doi: 10.1109/ACCESS.2022.3169284.
  22. G. N. Ahmad, H. Fatima, S. Ullah, A. Salah Saidi and Imdadullah, "Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV," in IEEE Access, vol. 10, pp. 80151-80173, 2022, doi: 10.1109/ACCESS.2022.3165792.
  23. Noura Ajam, “Heart Disease Diagnoses using Artificial Neural Network”, The International Insitute of Science, Technology and Education, vol.5, No.4, 2015, pp.7-11
  24. Dr.S.Seema Shedole, Kumari Deepika, “Predictive analytics to prevent and control chronic disease”, https://www.researchgate.net/punlication/316530782, January 2016.
  25. Mr. Chala Beyene, Prof. Pooja Kamat, “Survey on Prediction and Analysis the Occurrence of Heart Disease Using Data Mining Techniques”, International Journal of Pure and Applied Mathematics, 2018.
  26. 25Nimai Chand Das Adhikari, Arpana Alka, and rajat Garg, “HPPS: Heart Problem Prediction System using Machine Learning”.
  27. Muthuvel, Marimuthu & Abinaya, M & Hariesh, K & Madhankumar, K & Pavithra, V. (2018). A Review on Heart Disease Prediction using Machine Learning and Data Analytics Approach. International Journal of Computer Applications. 181. 975-8887. 10.5120/ijca2018917863.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-08-30

Issue

Volume 8, Issue 4, July-August-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Neha Madame, Prof. Mashhood Siddiqui, " A Review on Heart Disease Detection Using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.206-214, July-August-2022.