Examining Mortality Risk Prediction Using Machine Learning in Heart Failure Patients
DOI:
https://doi.org/10.31436/ijpcc.v11i1.561Keywords:
Blood creatinine, Ejection fraction, Logistic regression, random forests, gradient boosting, heart failureAbstract
Heart failure is fatal. Signs and symptoms of heart failure often overlap with those of other medical conditions. These symptoms could kill the patient. Predicting heart failure mortality helps healthcare workers spend resources to reduce or prevent deaths. Demographics, laboratory tests, and vital signs were used to create and test prediction models. This study compares random forests, and support vector machine to determine the best mortality risk prediction approach. This study analyses heart failure symptoms to identify risk factors for mortality. The study also examines how these findings apply to all heart failure patients. The study collects a subset of MIMIC-III heart failure patients to achieve this goal. Previous research studies used a smaller dataset, which is compared to this one. The experimental examination of blood creatinine, ejection fraction, and binned age shows that machine learning is be able to classify heart failure patients by mortality risk. This information helps clinicians improve treatment, improving patient outcomes and resource allocation. The study shows that machine learning can improve heart failure mortality risk prediction by using large clinical datasets like MIMIC-III. This study advances predictive analytics in healthcare, giving valuable information for clinicians and academics seeking to better heart failure patient care.
References
H. Shen, W. Ma, and Y. Wang, "A review on data preprocessing techniques for machine learning in big data era," Frontiers of Computer Science, vol. 17, no. 2, pp. 163–182, 2023. doi: 10.1007/s11704-023-10123-6.
A. Kumar, N. Goyal, and D. Singh, "Efficient prediction using machine learning techniques: A systematic review of challenges and methodologies," Applied Intelligence, vol. 52, no. 7, pp. 7284–7304, 2022. doi: 10.1007/s10489-021-02742-1.
S. P. Murphy, N. E. Ibrahim, and J. L. Januzzi, "Heart failure with reduced ejection fraction: A review," JAMA, vol. 324, no. 5, pp. 488–504, 2020.
M. M. Redfield, "Heart failure with preserved ejection fraction," New England Journal of Medicine, vol. 375, no. 19, pp. 1868–1877, 2016.
A. Forbes and H. Gallagher, "Chronic kidney disease in adults: Assessment and management," Clinical Medicine, vol. 20, no. 2, p. 128, 2020.
Ponikowski, P., Voors, A. A., Anker, S. D., Bueno, H., Cleland, J. G., Coats, A. J., … others. (2016). 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Kardiologia Polska (Polish Heart Journal), 74(10), 1037–1147.
J. Xu and M. Quaddus, "Managing Infrastructure for Information Systems," in Managing Information Systems: Ten Essential Topics, Paris: Atlantis Press, 2013, pp. 85–107. doi: 10.2991/978-94-91216-89-3_6.
W. Giere, "Electronic patient information–pioneers and MuchMore," Methods of Information in Medicine, vol. 43, no. 5, pp. 543–552, 2004.
A. Hollerbach, R. Brandner, A. Bess, P. Schmücker, and B. Bergh, "Electronically signed documents in health care," Methods of Information in Medicine, vol. 44, no. 4, pp. 520–527, 2005.
R. Haux, E. Ammenwerth, W. Herzog, and P. Knaup, "Health care in the information society. A prognosis for the year 2013," International Journal of Medical Informatics, vol. 66, no. 1–3, pp. 3–21, 2002.
W. Kirch, Ed., "Electronic Health Record (EHR)," in Encyclopedia of Public Health, Dordrecht: Springer Netherlands, 2008, pp. 326–326. doi: 10.1007/978-1-4020-5614-7_946.
D. Chicco and G. Jurman, "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone," BMC Medical Informatics and Decision Making, vol. 20, no. 1, pp. 1–16, 2020.
T. Ahmad, A. Munir, S. H. Bhatti, M. Aftab, and M. A. Raza, "Survival analysis of heart failure patients: A case study," PLoS One, vol. 12, no. 7, p. e0181001, 2017.
P. Y. Papalambros, "Design science: Why, what, and how," Design Science, vol. 1, p. e1, 2015. doi: 10.1017/dsj.2015.1.
A. E. Johnson, T. J. Pollard, L. Shen, L.-W. H. Li, M. Feng, M. Ghassemi, and R. G. Mark, "MIMIC-III, a freely accessible critical care database," Scientific Data, vol. 3, p. 160035, 2016.
N. Patel and S. Upadhyay, "Study of various decision tree pruning methods with their empirical comparison in WEKA," International Journal of Computer Applications, vol. 60, no. 12, 2012.
AMIA Annual Symposium Proceedings, vol. 2016, p. 844, 2016, American Medical Informatics Association
