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Volume 14
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10.3390/nu14194051
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Article

Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms

by
Shing-Hong Liu
1,
Zhi-Kai Yang
1,
Kuo-Li Pan
2,3,4,*,
Xin Zhu
5 and
Wenxi Chen
5,*
1
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan
2
Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi Branch, Chiayi City 61363, Taiwan
3
College of Medicine, Chang Gung University, Taoyuan City 33305, Taiwan
4
Heart Failure Center, Chang Gung Memorial Hospital, Chiayi Branch, Chiayi City 61363, Taiwan
5
Division of Information Systems, School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan
*
Authors to whom correspondence should be addressed.
Nutrients 2022, 14(19), 4051; https://doi.org/10.3390/nu14194051
Submission received: 2 September 2022 / Revised: 22 September 2022 / Accepted: 26 September 2022 / Published: 29 September 2022
(This article belongs to the Section Nutrition and Public Health)
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Abstract

It is estimated that 360,000 patients have suffered from heart failure (HF) in Taiwan, mostly those over the age of 65 years, who need long-term medication and daily healthcare to reduce the risk of mortality. The left ventricular ejection fraction (LVEF) is an important index to diagnose the HF. The goal of this study is to estimate the LVEF using the cardiovascular hemodynamic parameters, morphological characteristics of pulse, and bodily information with two machine learning algorithms. Twenty patients with HF who have been treated for at least six to nine months participated in this study. The self-constructing neural fuzzy inference network (SoNFIN) and XGBoost regression models were used to estimate their LVEF. A total of 193 training samples and 118 test samples were obtained. The recursive feature elimination algorithm is used to choose the optimal parameter set. The results show that the estimating root-mean-square errors (ERMS) of SoNFIN and XGBoost are 6.9 ± 2.3% and 6.4 ± 2.4%, by comparing with echocardiography as the ground truth, respectively. The benefit of this study is that the LVEF could be measured by the non-medical image method conveniently. Thus, the proposed method may arrive at an application level for clinical practice in the future.
Keywords: heart failure; left ventricular ejection fraction; cardiovascular hemodynamic parameter; morphological characteristic of pulse; machine learning heart failure; left ventricular ejection fraction; cardiovascular hemodynamic parameter; morphological characteristic of pulse; machine learning

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MDPI and ACS Style

Liu, S.-H.; Yang, Z.-K.; Pan, K.-L.; Zhu, X.; Chen, W. Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms. Nutrients 2022, 14, 4051. https://doi.org/10.3390/nu14194051

AMA Style

Liu S-H, Yang Z-K, Pan K-L, Zhu X, Chen W. Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms. Nutrients. 2022; 14(19):4051. https://doi.org/10.3390/nu14194051

Chicago/Turabian Style

Liu, Shing-Hong, Zhi-Kai Yang, Kuo-Li Pan, Xin Zhu, and Wenxi Chen. 2022. "Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms" Nutrients 14, no. 19: 4051. https://doi.org/10.3390/nu14194051

APA Style

Liu, S.-H., Yang, Z.-K., Pan, K.-L., Zhu, X., & Chen, W. (2022). Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms. Nutrients, 14(19), 4051. https://doi.org/10.3390/nu14194051

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