Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries

Abstract

This article presents a new approach to solving the problem of automated detection of myocardial infarction of various localization by electrocardiogram data entries. Only the second standard lead is used in the analysis. The signal in this lead undergoes digital filtering in order to remove low-frequency and high-frequency interference. Then, individual cardio complexes P-QRS-T are extracted from the signal, and the following parameters are calculated for them: minimum value, maximum value, interquartile range, mean absolute deviation, root mean square, mode, and entropy. Using the calculated parameters, a standardized training (learning) dataset is formed. The classifier model represents the k-nearest neighbors algorithm with the Manhattan metric of the distance between the objects and number of neighbors k = 9. After learning, the classifier shows the results by precision pre = 98.60%, by recall rec = 97.34%, by specificity spec = 95.93%, and by accuracy acc = 97.03%. According to the analysis of the obtained results, the suggested classifier model offers certain advantages as compared to existing alternatives.