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

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 (E-RMS) 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.

Automated summary

The goal of this study is to estimate left ventricular ejection fraction (LVEF) in heart failure patients using machine learning algorithms. By utilizing cardiovascular hemodynamic parameters, morphological characteristics of pulse, and bodily information, along with the self-constructing neural fuzzy inference network (SoNFIN) and XGBoost regression models, LVEF can be accurately estimated. The results demonstrate that this non-medical image method conveniently measures LVEF, potentially finding application in clinical practice.