{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T18:02:35Z","timestamp":1783620155116,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T00:00:00Z","timestamp":1663372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world\u2019s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.<\/jats:p>","DOI":"10.3390\/s22187033","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T04:49:22Z","timestamp":1663562962000},"page":"7033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2623-3294","authenticated-orcid":false,"given":"Dmytro","family":"Chumachenko","sequence":"first","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University Kharkiv Aviation Institute, 61072 Kharkiv, Ukraine"},{"name":"Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mykola","family":"Butkevych","sequence":"additional","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University Kharkiv Aviation Institute, 61072 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Lode","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9063-2682","authenticated-orcid":false,"given":"Marcus","family":"Frohme","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kurt J. G.","family":"Schmailzl","sequence":"additional","affiliation":[{"name":"ccc. Center for Connected Health Care UG, 16818 Wustrau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4501-7426","authenticated-orcid":false,"given":"Alina","family":"Nechyporenko","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany"},{"name":"Systems Engineering Department, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1108\/IJSSP-07-2020-0323","article-title":"The influence of the COVID-19 pandemic on the digital transformation of work","volume":"40","author":"Nagel","year":"2020","journal-title":"Int. J. Sociol. Soc. 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