Key result
A novel multi-label feature selection method based on kernelized fuzzy rough sets identified an optimal subset of 23 ECG features, achieving an Average Precision of 0.8053 and Micro-F1 of 0.5874.
Why the study?
Multi-class methods commonly applied to multi-label clinical ECG databases ignore correlations between diseases, causing information loss.
A novel multi-label feature selection method based on kernelized fuzzy rough sets effectively optimizes ECG feature space for multi-label arrhythmia classification.
Hypothesis-generating for ECG-based multi-label arrhythmia classification; requires prospective clinical validation before adoption.
Arrhythmia is a kind of cardiovascular disease that seriously threats human health, and intelligent analysis of electrocardiogram (ECG) is an effective method for the early prevention and precise treatment to arrhythmia. In clinical ECG databases, it is common to see the multi-label phenomenon that one patient would be labelled with multiple types of arrhythmia. However, the current research is mainly to use the multi-class methods for dealing with the problem of multi-label, ignoring the correlations between diseases and causing information loss. Therefore, this paper aims to propose a multi-label feature selection (MLFS) method based on ECG and design a novel evaluation criterion based on kernelized fuzzy rough sets so as to choose the optimal feature subset and optimize ECG feature space. Through sufficient experiments to prove the feasibility of our methods, we obtain the optimal feature subset composed of 23 ECG features. For the six evaluation criterions of multi-label learning, Average Precision is 0.8053, Hamming Loss is 0.1063, Ranking Loss is 0.1366, One-error is 0.2021, Coverage is 0.4018, and Micro-F1 is 0.5874. The outcome presents great superiority to the current algorithms of MLFS. This study is a prerequisite for implementing big data ECG classification diagnosis and disease modeling.
No takes yet. Share an insight, caveat, or question.
Li et al. (2020) studied Arrhythmia. Multi-label feature selection (MLFS) method vs. Current algorithms of MLFS was evaluated on Multi-label learning evaluation criteria (Average Precision, Hamming Loss, Ranking Loss, One-error, Coverage, Micro-F1). A novel multi-label feature selection method based on kernelized fuzzy rough sets identified an optimal subset of 23 ECG features, achieving an Average Precision of 0.8053 and Micro-F1 of 0.5874.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: