Key result
Hybrid HRV analysis using CS-RUSBoost achieves ~0.93 F1 score in classifying high-risk hypertensive patients.
Why the study?
Early detection of high-risk hypertensive patients is vital to prevent life-threatening vascular diseases, but identifying tenuous differences in HRV between low-risk and high-risk patients at an early stage remains challenging.
Does a hybrid HRV feature extraction and CS-RUSBoost algorithm improve the identification of high-risk hypertensive patients compared to other boosting algorithms?
Does a hybrid HRV feature extraction and CS-RUSBoost algorithm improve the identification of high-risk hypertensive patients compared to other boosting algorithms?
A novel machine learning approach using HRV features and the CS-RUSBoost algorithm effectively stratifies high-risk hypertensive patients, addressing data imbalance and ectopic beats.
May aid ML-based HRV stratification in hypertension; leaves open prospective outcome validation.
Hypertension is a global challenge to the public health which can easily lead to life-threatening vascular diseases unless control measures are adopted. Considering the prevalence of vascular diseases and their fatality, early detection of high-risk patients is an important problem in the present world. Heart rate variability (HRV) analysis can be an effective prognostic tool to identify the characteristics of vulnerable patients, considering its reliability in predicting sudden cardiac deaths. However, challenge lies in identifying tenuous differences in HRV between the low-risk and high-risk patients at the early stage. With this motivation, we propose a hybrid approach based on dual-tree complex wavelet packet transform (DTCWPT) and linear time domain as well as nonlinear analysis of HRV signal to extract multitudinous features. A key issue before the HRV analysis of such patients is the presence of marked amount of ectopic beats, which is addressed by using time-varying auto regressive (TVAR) technique. The features extracted from TVAR edited HRV signals are shortlisted by minimum redundancy maximum relevance algorithm for an efficient classifier modeling. Furthermore in this study, we propose to use cost-sensitive RUSBoost (CS-RUSBoost) algorithm for handling the class imbalance problem of the data. A comparative performance evaluation of CS-RUSBoost with RUSBoost, SMOTEBoost, asymmetric AdaBoost algorithm shows a superior result by CS-RUSBoost with G-mean of 0.9352 and F1 score of 0.9347.
No takes yet. Share an insight, caveat, or question.
Deka et al. (2021) studied Hypertension. CS-RUSBoost algorithm with hybrid HRV features vs. RUSBoost, SMOTEBoost, and asymmetric AdaBoost algorithms was evaluated on Classification performance (G-mean and F1 score). A hybrid heart rate variability analysis approach using the CS-RUSBoost algorithm achieved superior classification of high-risk hypertensive patients with a G-mean of 0.9352 and F1 score of 0.9347.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: