A machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitude

Basit Öğe Kaydı
dc.contributor.authorAgrawal, R.K.
dc.contributor.authorSewani, R.R.
dc.contributor.authorDelen, D.
dc.contributor.authorBenjamin, B.
dc.date.accessioned2024-05-19T14:33:38Z
dc.date.available2024-05-19T14:33:38Z
dc.date.issued2022
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAccording to the World Health Organization, Heart disease is the number one killer of humans, with coronary heart disease (CHD) being the most common type of heart disease. CHD leads to myocardial ischemia (MI) or infarction. Several clinical tests are available to assist physicians in diagnosing MI or infarcted (unhealthy) patients. However, diagnostic tests can be costly, invasive, and unreliable in identifying patients with declining coronary health conditions. This study investigated the application of Machine Learning (ML) techniques on the Vector Magnitude (VM) data of heart signals generated via Vectorcardiography (VCG) to classify unhealthy patients from healthy patients. Patients with MI, a CHD, are identified as ill patients. Three machine-learning classification techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), were applied to classify healthy and unhealthy (MI) patients. The heart signal dataset was acquired from the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic electrocardiogram (ECG) Database. A 10-fold cross-validation sampling method was used to improve the predictability of the sample. Results from ML techniques produced high classification sensitivity, specificity, and accuracy. ML analysis findings indicated that DT is the best predictor for classification accuracy, followed by SVM and ANN. The future study goal is to expand the study with forward-looking data and the right sample size for clinical validity and support the high accuracy results. © 2022 The Author(s)en_US
dc.description.sponsorshipThe authors express gratitude to the PTB database organization for their open access to the ECG signal database used to conduct this research ([20]).en_US
dc.identifier.doi10.1016/j.health.2022.100121
dc.identifier.issn2772-4425
dc.identifier.scopus2-s2.0-85160701304en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.health.2022.100121
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4293
dc.identifier.volume2en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofHealthcare Analyticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectClassificationen_US
dc.subjectCoronary Diseaseen_US
dc.subjectHeartrate Variabilityen_US
dc.subjectK-Fold Cross-Validationen_US
dc.titleA machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitudeen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu