Why the study?
Does k-NN classification using Cityblock distance metric improve accuracy of classifying magnetocardiography CDDM compared to other metrics?
Does k-NN classification using Cityblock distance metric improve accuracy of classifying magnetocardiography CDDM compared to other metrics?
Using the Cityblock distance metric in a k-NN classifier with 19-27 neighbors improves the accuracy of classifying magnetocardiography data by up to 10%.
Cityblock metric may aid k-NN magnetocardiography classification; leaves open validation in clinical cohorts.
Magnetocardiography is a sensitive technique of measuring low magnetic fields generated by heart functioning, which is used for diagnostics of large number of cardiovascular diseases. In this paper, k-nearest neighbor (k-NN)technique is used for binary classification of myocardium current density distribution maps (CDDM)from patients with negative T-peak, male and female patients with microvessels (diffuse) abnormalities and sportsmen, which are compared with normal control subjects. Number of neighbors for k-NN classifier was selected to obtain highest classification characteristics. Specificity, accuracy, precision and sensitivity of classification as functions of number of neighbors in k-NN are obtained for classification with several distance measures: Mahalanobis, Cityblock, Eucleadian and Chebyshev. Increase of the accuracy of classification for all groups up to 10% was obtained usingCityblock distance metric in binary k-NN classifier with 19 - 27 neighbors, comparing to other metrics. Obtained results are acceptable for further patient’s state evaluation.
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Udovychenko et al. (2015) studied this question.
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