Key result
A two-stage classification method combining correlation analysis and a k-Nearest Neighbor algorithm achieved an average accuracy of 96%, sensitivity of 70%, specificity of 98%, and precision of 70% for classifying magnetocardiographic maps of various heart states.
Why the study?
Magnetocardiography results provide useful information for diagnosing various heart diseases, but their usage remains undervalued in the cardiology community.
Can a two-stage classification using correlation analysis and a k-NN algorithm accurately classify current density distribution maps from magnetocardiography for various heart states?
Can a two-stage classification using correlation analysis and a k-NN algorithm accurately classify current density distribution maps from magnetocardiography for various heart states?
Effect estimate: 96% average accuracy
A k-NN based classification of magnetocardiography current density distribution maps can accurately distinguish between various normal and pathological heart states.
Magnetocardiography is a modern method of registration of the magnetic component of electromagnetic field, generated by heart activity. Magnetocardiography results are a useful source for the diagnosis of various heart diseases and states, but their usage is still undervalued in the cardiology community. In this study, a two-stage classification by correlation analysis using a k-Nearest Neighbor (k-NN) algorithm is applied for the binary classification of myocardium current density distribution maps (CDDMs). Fourteen groups of CDDMs from patients with different heart states, healthy volunteers, sportsmen, patients with negative T-peak, patients with myocardial damage, male and female patients with microvascular disease, patients with ischemic heart disease, and patients with left ventricular hypertrophy, divided into five and three different groups depending on the degree of pathology, were compared. Selection of best metric, used in classifier and number of neighbors, was performed to define the classifier with best performance for each pair of heart states. Accuracy, specificity, sensitivity, and precision values dependent on the number of neighbors are obtained for each class. The proposed method allows to obtain a value of average accuracy equal to 96%, 70% sensitivity, 98% specificity, and 70% precision.
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Udovychenko et al. (2021) studied Various heart states (ischemic heart disease, left ventricular hypertrophy, microvascular disease, etc.) (n=247). Two-stage classification using correlation analysis and k-Nearest Neighbor (k-NN) algorithm vs. One-stage multiclass classifier (historical comparison) was evaluated on Average classification accuracy across 14 heart state groups (96% average accuracy). A two-stage classification method combining correlation analysis and a k-Nearest Neighbor algorithm achieved an average accuracy of 96%, sensitivity of 70%, specificity of 98%, and precision of 70% for classifying magnetocardiographic maps of various heart states.
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