Open Access
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03007
Number of page(s) 7
Section Computing
Published online 09 August 2021
  1. M. A. Jabbar, P. Chandra, and B. L. Deekshatulu, “Prediction of risk score for heart disease using associative classification and hybrid feature subset selection,” Int. Conf. Intell. Syst. Des. Appl. ISDA,pp. 628–634, (2012). [Google Scholar]
  2. V. Kirubha and S. M. Priya, “Survey on Data Mining Algorithms in Disease Prediction,” vol. 38, no. 3, pp. 124–128, (2016). [Google Scholar]
  3. (23 October 2020) [Google Scholar]
  4. PushkalaV, A. T., & Angayarkanni, S. A. (2019). Comparative Study of Heart Disease Prediction Using Machine Learning Algorithms. International Journal of Innovations in Engineering and Technology (IJIET), 10, 2319–1058. [Google Scholar]
  5. (25 October 2020) [Google Scholar]
  6. T.Mythili, Dev Mukherji, Nikita Padaila and Abhiram Naidu, “A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)”, International Journal of Computer Applications, vol. 68, (16 April 2013). [Google Scholar]
  7. (28 October 2020) [Google Scholar]
  8. A. H. M. S. U. Marjia Sultana, “Analysis of Data Mining Techniques for Heart Disease Prediction,” (2018). [Google Scholar]
  9. M. I. K. A. I. S. Musfiq Ali, “Heart Disease using Machine Learning Algorithms. [Google Scholar]
  10. K. Bhanot, “,” (13 Feb 2019.) [Online]. Available: (2 November 2020). [Google Scholar]
  11. Abdullah AS, Rajalaxmi RR. A data mining model for predicting the coronary heart disease using random forest classifier. International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C 2012) Proceedings published in International Journal of Computer Applications® (IJCA); (2012). [Google Scholar]
  12. Lafta R, YanLi, Tseng VS. An Intelligent Recommender System based on Short Term Risk Prediction for Heart Disease patients. IEEE/WIC/ ACM International Conference on Web Intelligence and Intelligent Agent Technology.Singapore: IEEE; (2015). [Google Scholar]
  13. SonamNikhar, A.M. Karandikar, “Prediction of Heart Disease Using Machine Learning Algorithms”, International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June-2016] InfogainPublication ( ISSN: 2454-1311, (2016). [Google Scholar]
  14. (13 November 2020) [Google Scholar]
  15. *Coressponding author: ruby .hasan92@gmail. com*Coressponding author: ruby .hasan92@gmail. comDevansh Shah, Samir Patel, Santosh Kumar Bharti, “Heart Disease Prediction using Machine Learning Techniques”, Springer Nature Singapore Pte Ltd (2020).*Coressponding author: ruby .hasan92@gmail. com*Coressponding author: ruby .hasan92@gmail. com [Google Scholar]
  16. Archana Singh, Rakesh Kumar, “Heart Disease Prediction using Machine Learning Algorithms”, International Conference on Electrical & Electronics Engineering(ICE3-2020): 452–457. [Google Scholar]
  17. Patel, J., TejalUpadhyay, D., & Patel, S. (2015). Heart disease prediction using machine learning and data mining technique. Heart Disease, 7(1), 129–137. [Google Scholar]
  18. Jan, M., Awan, A. A., Khalid, M. S., & Nisar,S. (2018). Ensemble approach for developing a smart heart disease prediction system using classification algorithms. Research Reports in Clinical Cardiology, 9, 33–45. [Google Scholar]
  19. Li, J. P., Haq, A. U., Din, S. U., Khan, J., Khan, A., & Saboor, A. (2020). Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access, 8, 107562–107582. [Google Scholar]
  20. Mohan, S., Thirumalai, C., & Srivastava, G. (2018). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542–81554. [Google Scholar]
  21. Mr. Chala Beyene, Prof. Pooja Kamat, “Survey on Prediction and Analysis the Occurrence of H e a r t D i sease Using Data Mining Techniques”, International Journal of Pureand Applied Mathematics, (2018). [Google Scholar]
  22. Kavitha, B. S., & Siddappa, M. A Survey on Machine Learning Techniques to Predict Heartt Disease. [Google Scholar]
  23. Kumar, N. K., Sindhu, G. S., Prashanthi, D. K., & Sulthana, A. S. (2020, March). Analysis and prediction of cardio vascular disease using machine learning classifiers. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 15–21). IEEE. [Google Scholar]
  24. Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, K. (2018, March). Prediction of heart disease using machine learning. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1275–1278). IEEE. [Google Scholar]
  25. Sharma, H., & Rizvi, M. A. (2017). Prediction of heart disease using machine learning algorithms: A survey. International Journal on Recent and Innovation Trendsin Computing and Communication, 5(8),99–104. [Google Scholar]
  26. (27 November 2020) [Google Scholar]
  27. (16 December 2020) [Google Scholar]
  28. Obasi, T., & Omair Shafiq, M. (2019). Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases. 2019 IEEE International Conference on Big Data (Big Data). [Google Scholar]

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