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Machine learning-based detection of diabetes mellitus from singlelead electrocardiography:A phenotype-stratified approach 认领 引用 被引量：2
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作者 Anna Dmitrievna Karbovskaya Basheer Abdullah Marzoog +9 位作者 Anastasia Stroeva Alexander Suvorov Peter Chomakhidze Daria Gognieva Natalia Kuznetsova Philipp Kopylov Abromavich Syrkin Valentin V Fadeev Sevindzh M Ismailova Irina V PoluboyarinoVa 《World Journal of Cardiology》 2026年第3期78-92,共15页
BACKGROUND Diabetes mellitus(DM)and the related sequalae remains one of the most frequently reported cause of morbidity and mortality in our era.This returns to the non-sufficient screening methods for DM at early sta... BACKGROUND Diabetes mellitus(DM)and the related sequalae remains one of the most frequently reported cause of morbidity and mortality in our era.This returns to the non-sufficient screening methods for DM at early stages.AIM To assess the diagnostic capabilities of the parameters of single lead electrocardiography(ECG)in the diagnosis of DM utilizing machine learning model.METHODS A single center study involved 629 participants with vs without DM.All the study participants passed transthoracic echocardiography,fasting blood glucose measurement,standard 12-lead ECG recording,and single lead ECG registration using the Cardio-Qvark®device.A gradient boosting machine model,specifically the XGBoost implementation,was developed using R v4.2 and Python v3.10.The model was trained and validated using a novel cluster-stratified approach-training on three phenotypic clusters and testing on the fourth-to isolate DM-specific ECG signatures from confounding cardiovascular disease.RESULTS The cluster-stratified analysis revealed that the model performed best in cluster 4(patients with high DM prevalence and significant comorbidities),achieving a sensitivity of 75%,specificity of 83%,and an area under the curve of 88%.CONCLUSION This study demonstrates that a phenotype-stratified approach is crucial for effective ECG-based DM screening.By identifying a specific clinical profile(cluster 4:High comorbidity burden with preserved cardiac function),we developed a model that accurately detects DM from a single-lead ECG.This phenotype-specific strategy overcomes the confounding effect of cardiovascular disease,moving beyond one-size-fits-all algorithms towards a precise and clinically viable tool for non-invasive DM detection in high-risk populations. 展开更多
关键词 Diabetes mellitus Machine learning model Diagnosis Single lead electrocardiography Hyperglycemia Metabolic syndrome
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Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression 认领 引用 被引量：1
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作者 Anna Dmitrievna Karbovskaya Basheer Abdullah Marzoog +9 位作者 Anastasia Stroeva Peter Chomakhidze Daria Gognieva Natalia Kuznetsova Alexander Suvorov Philipp Kopylov Abromavich Syrkin Valentin V Fadeev Irina V PoluboyarinoVa Sevindzh M Ismailova 《World Journal of Cardiology》 2026年第3期93-106,共14页
BACKGROUND Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus(DM).AIM To investigate any association between single-lead elec... BACKGROUND Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus(DM).AIM To investigate any association between single-lead electrocardiography(ECG)parameters and the diagnosis of DM.METHODS A single center study involved participants of Caucasian origin for the period between May 2,2022 and August 23,2025 with or without DM and aged≥18 years.All participants participating in the study passed the cardiologist's,random glucose measurement using a glucometer,single lead-ECG registration(using Cardio-Qvark®)and transthoracic echocardiography.Statistical analysis conducted using the R programming language(version 4.5).RESULTS The built logistic regression machine learning model demonstrated diagnostic performance in discriminating(area under the curve)type 1 DM 0.84(95%CI:0.76-0.91),type 2 DM 0.69(95%CI:0.61-0.76),and healthy control 0.82(95%CI:0.76-0.87).CONCLUSION The developed model demonstrates an association between single-lead ECG parameters and diabetes status that can support the clinical identification of individuals who would benefit from confirmatory testing.This is probably attributable to relatively stable and long-term physiological alterations associated with the state of the disease. 展开更多
关键词 Machine learning Diabetes mellitus Diagnosis Hyperglycemia Glycated hemoglobin Glucose
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