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An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana
10.5120/ijca2020920741

Md. Shafiul Azam, Md. Abu Raihan and Humayan Kabir Rana. An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction. International Journal of Computer Applications 175(21):16-21, September 2020. BibTeX

@article{10.5120/ijca2020920741,
	author = {Md. Shafiul Azam and Md. Abu Raihan and Humayan Kabir Rana},
	title = {An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2020},
	volume = {175},
	number = {21},
	month = {Sep},
	year = {2020},
	issn = {0975-8887},
	pages = {16-21},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume175/number21/31576-2020920741},
	doi = {10.5120/ijca2020920741},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

According to recent survey of WHO (World Health Organization) 17.9 million people die each year because of heart related diseases and it is increasing rapidly. With the increasing population and diseases, it has become challenging to diagnosis and treatment diseases at the right time. But there is a light of hope that recent advancements in technology have accelerated the public health sector by advanced functional biomedical solutions. This paper aims to analyze the various machine learning approaches namely Naïve Bayes (NB), Random Forest (RF) Classification, Decision tree (DT), Support Vector Machine (SVM) and Logistic Regression (LR) by employing a qualified dataset for heart disease prediction. This research finds the correlations between the various attributes that are suitable to predict the chances of a heart disease and compares the impact of Principle Component Analysis (PCA) on the accuracy of the above mentioned algorithms.

References

  1. Meijers, Wouter C., and Rudolf A. de Boer. "Common risk factors for heart failure and cancer." Cardiovascular research vol.115, no.5, pp. 844-853, 2019.
  2. NK Podder, HK Rana and et al., "A system biological approach to investigate the genetic profiling and comorbidities of type 2 diabetes", Gene Reports, pp. 100830, https://doi.org/10.1016/j.genrep.2020.100830, 2020.
  3. WHO, "Cardiovascular diseases (CVDs)", 2017. [Online].who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). [Accessed: 22- Aug- 2020].
  4. Sa, S., "Intelligent heart disease prediction system using data mining techniques.", International Journal of healthcare & biomedical Research, vol. 1, pp. 94-101, 2013.
  5. Latha, C. Beulah Christalin, and S. Carolin Jeeva. "Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques." Informatics in Medicine Unlocked 16 (2019): 100203.
  6. Rana, H. K., Azam, M. S., &Akhtar, M. R., "Iris recognition system using PCA based on DWT". SM Journal of Biometrics & Biostatistics, vol. 2, no. 3, p. 1015, 2017.
  7. Rana, H. K., Azam, M. S., Akhtar, M. R., Quinn, J. M., &Moni, M. A.,"A fast iris recognition system through optimum feature extraction.",PeerJ Computer Science, vol. 5, p. e184, 2019.
  8. HosmerJr, David W., Stanley Lemeshow, and Rodney X. Sturdivant. "Applied logistic regression". vol. 398. John Wiley & Sons, 2013.
  9. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., &Scholkopf, B., "Support Vector Machines", IEEE Intelligent Systems and their applications, vol. 13, no. 4, pp. 18-28, 1998.
  10. Jony, M. H., Johora, F. T., Khatun, P., &Rana, H. K., "Detection of Lung Cancer from CT Scan Images using GLCM and SVM", In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1-6, IEEE, 2019.
  11. Johora, F. T., Jony, M. H., Hossain, M. S., &Rana, H. K. "Lung Cancer Detection Using Marker Controlled Watershed with SVM", GUB Journal of Science and Engineering, vol. 5, no. 1, pp. 24-30, 2018.
  12. Azam, M.S. &Rana H.K., "Iris Recognition using Convolutional Neural Network", International Journal of Computer Applications, vol. 175, no. 12, pp. 24-28, 2020.
  13. Hossen, M. R., Azam, M. S., &Rana, H. K., "Performance evaluation of various DNA pattern matching algorithms using different genome datasets", Pabna University of Science and Technology Studies, vol. 3, no. 1, pp. 14-8, 2018.
  14. Mujtaba, M. A., Azam, M. S., &Rana, H. K., "Performance evaluation of various data mining classification techniques that correctly classify banking transaction as fraudulent", GUB Journal of Science and Engineering, vol. 4, no. 1, pp. 59-63, 2017.
  15. Maheswari, S., &Pitchai, R. "Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm", Current Medical Imaging, vol. 15, no. 8, pp. 712-717, 2019.
  16. Iliyas, M. M. K., &Shaikh, M. I. S. "Prediction of Heart Disease Using Decision Tree", AllanaInst of Management Sciences, Pune, vol. 9, pp. 1-5, 2019.
  17. Joloudari, J. H., HassannatajJoloudari, E., Saadatfar, H., GhasemiGol, M., Razavi, S. M., Mosavi, A., &Nadai, L. "Coronary artery disease diagnosis; ranking the significant features using a random trees model", International journal of environmental research and public health, vol. 17, no. 3, p. 731, 2020.
  18. Vallée, A., Petruescu, L., Kretz, S., Safar, M. E., &Blacher, J. "Added value of aortic pulse wave velocity index in a predictive diagnosis decision tree of coronary heart disease", American journal of hypertension, vol. 32, no. 4, pp. 375-383, 2019.
  19. Latha, C. Beulah Christalin, and S. Carolin Jeeva. "Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques." Informatics in Medicine Unlocked 16 (2019): 100203.

Keywords

Heart Disease, Machine Learning Algorithms, PCA, Decision Tree, SVM, Random Forest, Logistic Regression, Naïve Bayes