Performance Evaluation of Machine Learning Methods for Heart Failure Prediction

Jing Xia; Xiaoying Wang

doi:doi:10.11648/j.ajcem.20231102.12

| Peer-Reviewed

Performance Evaluation of Machine Learning Methods for Heart Failure Prediction

Jing Xia, Xiaoying Wang

Published in American Journal of Clinical and Experimental Medicine (Volume 11, Issue 2)

Received: 21 February 2023 Accepted: 22 March 2023 Published: 31 March 2023

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.

Published in	American Journal of Clinical and Experimental Medicine (Volume 11, Issue 2)
DOI	10.11648/j.ajcem.20231102.12
Page(s)	33-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Mining, Prediction, Classification Models, Heart Failure, Clinical Medicine

References

[1]	Ahmad, T., Munir, A., Bhatti, S. H., Aftab, M., & Raza, M. A. (2017). Survival analysis of heart failure patients: A case study. PloS One, 12 (7), e0181001.
[2]	Animut, K., & Berhanu, G. (2022). Determinants of anemia status among pregnant women in ethiopia: using 2016 ethiopian demographic and health survey data; application of ordinal logistic regression models. BMC Pregnancy and Childbirth, doi: 10.1186/S12884-022-04990-8.
[3]	Augustine, K. A., Pascal, K., K., Faustina, A., et al. (2022). A binary logistic regression analysis on the factors associated with high blood pressure and its related heart issues. Science Journal of Applied Mathematics and Statistics, doi: 10.11648/J.SJAMS.20221003.12.
[4]	Bleumink, G. S., Knetsch, A. M., Sturkenboom, M. C., et al. (2004). Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure: the Rotterdam study. European Heart Journal, 25 (18), 1614-1619.
[5]	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), 1-16.
[6]	Hehde, J., Olschinsky-Szermer, M., Pahl, J., et al. (2022). Indirectly determined hematology reference intervals for pediatric patients in Berlin and Brandenburg. Clinical Chemistry and Laboratory Medicine (CCLM), 60 (3), 408-432.
[7]	Hu, J., Fei, Y., & Li, W. Q. (2021). Predicting the mortality risk of acute respiratory distress syndrome: radial basis function artificial neural network model versus logistic regression model. Journal of clinical monitoring and computing, doi: 10.1007/S10877-021-00716-X.
[8]	Lafta, R., Zhang, J., Tao, X., Li, Y., Tseng, V. S., Luo, Y., & Chen, F. (2016). An intelligent recommender system based on predictive analysis in telehealthcare environment. Web Intelligences, 4 (4), 325-336.
[9]	Ledley, R. S. & Lusted, L. B. (1959). Reasoning foundations of medical diagnosis: symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science, 130 (3366), 9-21.
[10]	McCullough, P. A., Jurkovitz, C. T., Pergola, P. E., et al. (2007). Independent components of chronic kidney disease as a cardiovascular risk state: results from the Kidney Early Evaluation Program (KEEP). Archives of Internal Medicine, 167 (11), 1122-1129.
[11]	Poolsawad, N., Moore, L., Kambhampati, C., & Cleland, J. G. (2014). Issues in the mining of heart failure datasets. International Journal of Automation and Computing, 11, 162-179.
[12]	Rammal, H. F., & Emam, A. Z. (2018). Heart failure prediction models using big data techniques. International Journal of Advanced Computer Science and Applications, 9 (5), doi: 10.14569/IJACSA.2018.090547.
[13]	Shirono, T., Niizeki, T., Iwamoto, H., et al. (2021). Therapeutic outcomes and prognostic factors of unresectable intrahepatic cholangiocarcinoma: a data mining analysis. Journal of Clinical Medicine, 10 (5), 987.
[14]	Sohrabi, B., Vanani, I. R., Gooyavar, A., & Naderi, N. (2019). Predicting the readmission of heart failure patients through data analytics. Journal of Information & Knowledge Management, 18 (01), 1950012.
[15]	Tran, M. T., & Lee, G. S. (2019). Super-resolution in music score images by instance normalization. Smart Media Journal, 8 (4), 64-71.

Cite This Article

Plain Text BibTeX RIS

APA Style

Jing Xia, Xiaoying Wang. (2023). Performance Evaluation of Machine Learning Methods for Heart Failure Prediction. American Journal of Clinical and Experimental Medicine, 11(2), 33-38. https://doi.org/10.11648/j.ajcem.20231102.12

Copy | Download

ACS Style

Jing Xia; Xiaoying Wang. Performance Evaluation of Machine Learning Methods for Heart Failure Prediction. Am. J. Clin. Exp. Med. 2023, 11(2), 33-38. doi: 10.11648/j.ajcem.20231102.12

Copy | Download

AMA Style

Jing Xia, Xiaoying Wang. Performance Evaluation of Machine Learning Methods for Heart Failure Prediction. Am J Clin Exp Med. 2023;11(2):33-38. doi: 10.11648/j.ajcem.20231102.12

Copy | Download

@article{10.11648/j.ajcem.20231102.12,
  author = {Jing Xia and Xiaoying Wang},
  title = {Performance Evaluation of Machine Learning Methods for Heart Failure Prediction},
  journal = {American Journal of Clinical and Experimental Medicine},
  volume = {11},
  number = {2},
  pages = {33-38},
  doi = {10.11648/j.ajcem.20231102.12},
  url = {https://doi.org/10.11648/j.ajcem.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20231102.12},
  abstract = {Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Performance Evaluation of Machine Learning Methods for Heart Failure Prediction
AU  - Jing Xia
AU  - Xiaoying Wang
Y1  - 2023/03/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ajcem.20231102.12
DO  - 10.11648/j.ajcem.20231102.12
T2  - American Journal of Clinical and Experimental Medicine
JF  - American Journal of Clinical and Experimental Medicine
JO  - American Journal of Clinical and Experimental Medicine
SP  - 33
EP  - 38
PB  - Science Publishing Group
SN  - 2330-8133
UR  - https://doi.org/10.11648/j.ajcem.20231102.12
AB  - Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.
VL  - 11
IS  - 2
ER  -

Copy | Download

Author Information

Jing Xia

Medical School, Jiangnan University, Wuxi, China
Xiaoying Wang

Medical School, Jiangnan University, Wuxi, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Jing Xia, Xiaoying Wang. (2023). Performance Evaluation of Machine Learning Methods for Heart Failure Prediction. American Journal of Clinical and Experimental Medicine, 11(2), 33-38. https://doi.org/10.11648/j.ajcem.20231102.12

Copy | Download

ACS Style

Jing Xia; Xiaoying Wang. Performance Evaluation of Machine Learning Methods for Heart Failure Prediction. Am. J. Clin. Exp. Med. 2023, 11(2), 33-38. doi: 10.11648/j.ajcem.20231102.12

Copy | Download

AMA Style

Jing Xia, Xiaoying Wang. Performance Evaluation of Machine Learning Methods for Heart Failure Prediction. Am J Clin Exp Med. 2023;11(2):33-38. doi: 10.11648/j.ajcem.20231102.12

Copy | Download

@article{10.11648/j.ajcem.20231102.12,
  author = {Jing Xia and Xiaoying Wang},
  title = {Performance Evaluation of Machine Learning Methods for Heart Failure Prediction},
  journal = {American Journal of Clinical and Experimental Medicine},
  volume = {11},
  number = {2},
  pages = {33-38},
  doi = {10.11648/j.ajcem.20231102.12},
  url = {https://doi.org/10.11648/j.ajcem.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20231102.12},
  abstract = {Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Performance Evaluation of Machine Learning Methods for Heart Failure Prediction
AU  - Jing Xia
AU  - Xiaoying Wang
Y1  - 2023/03/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ajcem.20231102.12
DO  - 10.11648/j.ajcem.20231102.12
T2  - American Journal of Clinical and Experimental Medicine
JF  - American Journal of Clinical and Experimental Medicine
JO  - American Journal of Clinical and Experimental Medicine
SP  - 33
EP  - 38
PB  - Science Publishing Group
SN  - 2330-8133
UR  - https://doi.org/10.11648/j.ajcem.20231102.12
AB  - Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.
VL  - 11
IS  - 2
ER  -

Copy | Download