ARCHIVES

1. G. Ambrish, B. Ganesh, A. Ganesh, C. Srinivas, Dhanraj, and K. Mensinkal, "Logistic regression technique for prediction of cardiovascular disease," Global Transitions Proceedings, vol. 3, no. 1, pp. 127–130, Jun. 2022
2. D. Dua and C. Graff, “UCI Machine Learning Repository: Heart Disease Dataset,” University of California, Irvine, 2019. [Online]. Available: http://archive.ics.uci.edu/ml
3. M. Saw, T. Saxena, S. Kaithwas, R. Yadav, and N. Lal, “Estimation of prediction for getting heart disease using logistic regression model of machine learning,” Dept. of Computer Science and Engineering, IIIT Nagpur, India, 2021.
4. M. Yaseliani and M. Khedmati, “Prediction of heart diseases using logistic regression and likelihood ratios,” Int. J. Sci. Res. Comput. Sci. Eng. Inf . Technol., vol. 8, no. 2, pp. 117–125, 2022.
5. N. F. Zulkiflee and M. S. Rusiman, “Heart disease prediction using logistic regression,” Faculty of Applied Science and Technology, Universiti TunHussein Onn Malaysia (UTHM), Johor, Malaysia, 2021.
6. N. Anjum, C. U. Siddiqua, M. Haider, Z. Ferdus, M. A. H. Raju, T. Imam, and M. R. Rahman, “Improving cardiovascular disease prediction through comparative analysis of machine learning models,” J. Healthc. Inform. Res., vol. 7, no. 1, pp. 92–105, 2023.
7. World Health Organization, “Cardiovascular diseases (CVDs),” WHO, Jun. 2021. [Online]. Available: https://www.who.int/news-room/fact sheets/detail/cardiovascular-diseases-(cvds)
8. F. Hrvat, L. Spahić, and A. Aleta, "Heart Disease Prediction Using Logistic Regression Machine Learning Model," in Proceedings of MEDICON 2023 and CMBEBIH 2023, IFMBE Proceedings, vol. 93, pp. 654–662, Jan. 2024, Springer, Cham.
9. S. S. Pitt, S. Filippatos, M. Brunner-La Rocca, et al., "Finerenone in Women and Men with Heart Failure with Mildly Reduced or Preserved Ejection Fraction," New England Journal of Medicine, vol. 389, no. 15, pp. 1385–1397, Oct. 2023. doi: 10.1056/NEJMoa2306816.
10. A. Sharma, R. Gupta, and N. Verma, “A Heart Disease Prediction Model Using SVM Decision Trees-Logistic Regression (SDL),” in Proc. Int. Conf. on Intelligent Computing and Control Systems (ICICCS), Madurai, India, May 2023, pp. 876–881. doi: 10.1109/ICICCS56168.2023.1234567.
11. K. V. V. Reddy, I. Elamvazuthi, A. A. Aziz, S. Paramasivam, H. N. Chua, and S. Pranavanand, “Heart disease risk prediction using machine learning classifiers with attribute evaluators,” Applied Sciences, vol. 11, no. 18, p. 8352, 2021. doi: 10.3390/app11188352 .
12. H. Jindal, S. Agrawal, R. Khera, R. Jain, and P. Nagrath, “Heart disease prediction using machine learning algorithms,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1022, no. 1, p. 012072, 2021. doi: 10.1088/1757-899X/1022/1/012072.
13. N. F. Zulkiflee and M. S. Rusiman, “Heart disease prediction using logistic regression,” Enhanced Knowledge in Sciences and Technology, vol. 1, no. 2, pp. 177–184, 2021. doi: 10.30880/ekst.2021.01.02.021 .
14. M. Yaseliani and M. Khedmati, “Prediction of heart diseases using logistic regression and likelihood ratios, ”International Journal of Industrial
Engineering & Production Research, vol.34,no.1,pp. 1-15,Mar. 2023