Table 7 Efficiency of the RF compared to the other published article

In our current study

98.04%

M. I. Hossain et al., “Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison,” Iran J. Comput. Sci., 2023, https://doi.org/10.1007/s42044-023-00148-7

97.7%

M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, and M. A. Moni, “Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison,” Comput. Biol. Med., vol. 136, no. May, p. 104,672, 2021, https://doi.org/10.1016/j.compbiomed.2021.104672

100%

A. S. S. N. K. Kumar, G. S. Sindhu, D. K. Prashanthi, “‘Analysis and prediction of cardio vascular disease using machine learning classifiers,’ in Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS).,” IEEE

85.71%

Fahim, K. E., Yassin, H., Amin, M. H., Dewan, P. D., & Islam, A. (2022, September). Detection of Cardiovascular Disease of Patients at an Early Stage Using Machine Learning Algorithms. In 2022 International Conference on Healthcare Engineering (ICHE) (pp. 1–6). IEEE

73.03%

Hossen, M. A., Tazin, T., Khan, S., Alam, E., Sojib, H. A., Monirujjaman Khan, M., & Alsufyani, A. (2021). Supervised machine learning-based cardiovascular disease analysis and prediction. Mathematical Problems in Engineering, 2021, 1–10

80%

Nashif, S., Raihan, M. R., Islam, M. R., & Imam, M. H. (2018). Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World Journal of Engineering and Technology, 6(4), 854–873

95.76%