Comparative Study on Prediction of Survival Event of Heart Failure Patients Using Machine Learning and Statistical Algorithms

O. E. Oyewunmi, O. B. Aladeniyi, O. K. Bodunwa


In a pressing global health concern with substantial morbidity and mortality rates, accurate survival prediction is paramount for informed decision-making and enhanced patient well-being. This study presented a comparative investigation aimed at predicting the survival events of heart failure (HF) patients through the utilization of both machine learning and statistical algorithms. A comprehensive dataset drawn from Allied Hospital and the Faisalabad Institute of Cardiology, Faisalabad, Pakistan, was used. The Synthetic Minority Over-Sampling Technique (SMOTE) was employed on the data to rectify the imbalance, and a notable improvement was observed. To ascertain significant variables, statistical methods (Mann-Whitney and Chi-Square) were compared with machine learning-based feature selection to identify pivotal features for survival prediction, namely ejection fraction and serum creatinine. Remarkably, on final training with these features, the Random Forest Classifier emerges as the top-performing model, boasting an accuracy exceeding 90%. These findings hold the potential to substantially enhance patient prognosis, management, and outcomes, consequently alleviating the strain on healthcare systems.


Doi: 10.28991/SciMedJ-2023-05-02-01

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Survival Prediction; Heart Failure; Machine Learning; Statistical Algorithms; Robust Predictor.


CDC. (2023). Heart Failure. Centers for Disease Control and Prevention. Centers for Disease Control and Prevention. U.S. Department of Health and Human Services. Available online: (accessed on March 2023).

WebMD. (2023). Heart Attack. WebMD LLC. Available online: (accessed on March 2023).

CDC. (2023). Health Topics – Heart Disease and Heart Attack. Centers for Disease Control and Prevention. U.S. Department of Health and Human Services. Available online: (accessed on March 2023).

WHO. (2023). Cardiovascular diseases (CVDs). World Health Organization. Available online: (accessed on March 2023).

Sawano, M., Shiraishi, Y., Kohsaka, S., Nagai, T., Goda, A., Mizuno, A., Sujino, Y., Nagatomo, Y., Kohno, T., Anzai, T., Fukuda, K., & Yoshikawa, T. (2018). Performance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides. ESC Heart Failure, 5(4), 610–619. doi:10.1002/ehf2.12278.

Cooper, L. B., & Hernandez, A. F. (2015). Assessing the Quality and Comparative Effectiveness of Team-Based Care for Heart Failure. Who, What, Where, When, and How. Heart Failure Clinics, 11(3), 499–506. doi:10.1016/j.hfc.2015.03.011.

Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20(1), 16. doi:10.1186/s12911-020-1023-5.

Rahimi, K., Bennett, D., Conrad, N., Williams, T. M., Basu, J., Dwight, J., Woodward, M., Patel, A., McMurray, J., & MacMahon, S. (2014). Risk Prediction in Patients with Heart Failure. JACC: Heart Failure, 2(5), 440–446. doi:10.1016/j.jchf.2014.04.008.

Edgar, T.W., & Manz, D.O. (2017). Research methods for cyber security. Research Methods for Cyber Security: Syngress, 428.

Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70. doi:10.1016/j.cogr.2023.04.001.

Pocock, S. J., Ariti, C. A., McMurray, J. J. V., Maggioni, A., Køber, L., Squire, I. B., Swedberg, K., Dobson, J., Poppe, K. K., Whalley, G. A., & Doughty, R. N. (2013). Predicting survival in heart failure: A risk score based on 39 372 patients from 30 studies. European Heart Journal, 34(19), 1404–1413. doi:10.1093/eurheartj/ehs337.

Tohyama, T., Ide, T., Ikeda, M., Kaku, H., Enzan, N., Matsushima, S., Funakoshi, K., Kishimoto, J., Todaka, K., & Tsutsui, H. (2021). Machine learning-based model for predicting 1 year mortality of hospitalized patients with heart failure. ESC Heart Failure, 8(5), 4077–4085. doi:10.1002/ehf2.13556.

Newaz, A., Ahmed, N., & Shahriyar Haq, F. (2021). Survival prediction of heart failure patients using machine learning techniques. Informatics in Medicine Unlocked, 26, 100772. doi:10.1016/j.imu.2021.100772.

Zahid, F. M., Ramzan, S., Faisal, S., & Hussain, I. (2019). Gender based survival prediction models for heart failure patients: A case study in Pakistan. PLoS ONE, 14(2), 1-10. doi:10.1371/journal.pone.0210602.

Mamun, M., Farjana, A., Al Mamun, M., Ahammed, M. S., & Rahman, M. M. (2022). Heart failure survival prediction using machine learning algorithm: am I safe from heart failure?. In 2022 IEEE World AI IoT Congress (AIIoT), 194-200. doi:10.1109/AIIoT54504.2022.9817303

Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018). eDoctor: machine learning and the future of medicine. Journal of Internal Medicine, 284(6), 603–619. doi:10.1111/joim.12822.

Al Mehedi Hasan, M., Shin, J., Das, U., & Yakin Srizon, A. (2021). Identifying Prognostic Features for Predicting Heart Failure by Using Machine Learning Algorithm. ACM International Conference Proceeding Series, 40–46. doi:10.1145/3460238.3460245.

Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., Van Rosendael, A. R., Beecy, A. N., Berman, D. S., Leipsic, J., Nieman, K., Andreini, D., Pontone, G., Schoepf, U. J., Shaw, L. J., Chang, H. J., Narula, J., … Min, J. K. (2019). Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal, 40(24), 1975–1986. doi:10.1093/eurheartj/ehy404.

Benjamins, J. W., Hendriks, T., Knuuti, J., Juarez-Orozco, L. E., & van der Harst, P. (2019). A primer in artificial intelligence in cardiovascular medicine. Netherlands Heart Journal, 27(9), 392–402. doi:10.1007/s12471-019-1286-6.

Fernandes, K., Chicco, D., Cardoso, J. S., & Fernandes, J. (2018). Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PeerJ Computer Science, 2018(5), 1-20. doi:10.7717/peerj-cs.154.

Virani, S. S., Alonso, A., Aparicio, H. J., Benjamin, E. J., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Cheng, S., Delling, F. N., Elkind, M. S. V., Evenson, K. R., Ferguson, J. F., Gupta, D. K., Khan, S. S., Kissela, B. M., Knutson, K. L., Lee, C. D., Lewis, T. T., … Tsao, C. W. (2021). Heart Disease and Stroke Statistics - 2021 Update: A Report from the American Heart Association. Circulation, 143(8), E254–E743. doi:10.1161/CIR.0000000000000950.

Kaggle. (2019). Heart Failure Prediction. Kaggle. Available online: (accessed on March 2023).

UCI Machine Learning Repository (2020). Heart Failure Clinical Records. Irvine, California, United States. Available online: (accessed on March 2023).

Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. (2018). Learning from imbalanced data sets Springer, Vol. 10, 377. doi:10.1007/978-3-319-98074-4.

Patidar, S., Kumar, D., & Rukwal, D. (2022). Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction. Advances in Transdisciplinary Engineering, 27, 64–69. doi:10.3233/ATDE220723.

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. doi:10.1109/ACCESS.2020.3001149.

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DOI: 10.28991/SciMedJ-2023-05-02-01


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