Performance Evaluation of Machine Learning Methods for Heart Failure Prediction

Jing Xia; Xiaoying Wang

doi:doi:10.11648/j.ajcem.20231102.12

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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

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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), 2023. Published by Science Publishing Group

Keywords

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

References

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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

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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

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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

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@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}
}

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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  -

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Author Information

Jing Xia

Medical School, Jiangnan University, Wuxi, China
Xiaoying Wang

Medical School, Jiangnan University, Wuxi, China

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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

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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