An automated system using a support vector machine linear kernel to analyze heart rate variability signals detected congestive heart failure with 93.1% accuracy and an AUC of 0.97.
Does an automated machine learning system using multimodal HRV features accurately detect congestive heart failure?
An automated machine learning approach using multimodal HRV features can effectively detect congestive heart failure with high accuracy.
Effect estimate: AUC 0.97 (95% CI 0.04-0.89)
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI lower bound = 0.04, upper bound = 0.89), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI lower bound 0.07, upper bound = 0.81) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI lower bound = 0.07, upper bound = 0.86). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.
Hussain et al. (Wed,) conducted a other in Congestive heart failure. Automated system analyzing HRV signals using machine learning (SVM linear kernel) was evaluated on Detection performance (accuracy and AUC) (AUC 0.97, 95% CI 0.04-0.89). An automated system using a support vector machine linear kernel to analyze heart rate variability signals detected congestive heart failure with 93.1% accuracy and an AUC of 0.97.