Using The Machine Learning Method in Portable ECG Monitoring System to Identify Diagnostically Complex Heart Diseases

Due to the high level of disability and death caused by heart diseases worldwide, the problem of combating cardiovascular diseases is becoming more and more important and relevant. In fact, one of the leading causes of death worldwide has long been cardiovascular disease, according to the World Health Organization. Among heart diseases, the most dangerous to life is myocardial infarction. In connection with the rapid morbidity, early diagnosis of this disease is one of the topical problems of modern public health care. At present, although there is a wide range of different statistical, math methods and computer tools for the processing of cardiological information, the task of increasing the accuracy of methods for the analysis of electrocardiograms and diagnostic tools in this field of study remained relevant. Taking into account the imperfect tools and systems for diagnosing heart diseases, as well as the human factor, the lack of a method that can help doctors to diagnose myocardial infarction early requires the development of new diagnostic information methods. This article presents a neural network analysis methodology for diagnosing myocardial infarction based on the joint analysis of direct and reverse signs of myocardial infarction from electrocardiographic information obtained from a portable cardiac analyzer.