TY - JOUR

T1 - Features characterising cardiac autonomic neuropathy in diabetes using ensembled classification

AU - Nedergaard, Rasmus Bach

AU - Scott, Matthew

AU - Wegeberg, Anne-Marie

AU - Okdahl, Tina

AU - Størling, Joachim

AU - Brock, Birgitte

AU - Drewes, Asbjørn Mohr

AU - Brock, Christina

N1 - Publisher Copyright: © 2023 International Federation of Clinical Neurophysiology

PY - 2023

Y1 - 2023

N2 - Objective: Using supervised machine learning to classify the severity of cardiovascular autonomic neuropathy (CAN). The aims were 1) to investigate which features contribute to characterising CAN 2) to generate an ensembled set of features that best describes the variation in CAN classification. Methods: Eighty-two features from demographic, beat-to-beat, biochemical, and inflammation were obtained from 204 people with diabetes and used in three machine-learning-classifiers, these are: support vector machine, decision tree, and random forest. All data were ensembled using a weighted mean of the features from each classifier. Results: The 10 most important features derived from the domains: Beat-to-beat, inflammation markers, disease-duration, and age. Conclusions: Beat-to-beat measures associate with CAN as diagnosis is mainly based on cardiac reflex responses, disease-duration and age are also related to CAN development throughout disease progression. The inflammation markers may reflect the underlying disease process, and therefore, new treatment modalities targeting systemic low-grade inflammation should potentially be tested to prevent the development of CAN. Significance: Cardiac reflex responses should be monitored closely to diagnose and classify severity levels of CAN accurately. Standard clinical biochemical analytes, such as glycaemic level, lipidic level, or kidney function were not included in the ten most important features. Beat-to-beat measures accounted for approximately 60% of the features in the ensembled data.

AB - Objective: Using supervised machine learning to classify the severity of cardiovascular autonomic neuropathy (CAN). The aims were 1) to investigate which features contribute to characterising CAN 2) to generate an ensembled set of features that best describes the variation in CAN classification. Methods: Eighty-two features from demographic, beat-to-beat, biochemical, and inflammation were obtained from 204 people with diabetes and used in three machine-learning-classifiers, these are: support vector machine, decision tree, and random forest. All data were ensembled using a weighted mean of the features from each classifier. Results: The 10 most important features derived from the domains: Beat-to-beat, inflammation markers, disease-duration, and age. Conclusions: Beat-to-beat measures associate with CAN as diagnosis is mainly based on cardiac reflex responses, disease-duration and age are also related to CAN development throughout disease progression. The inflammation markers may reflect the underlying disease process, and therefore, new treatment modalities targeting systemic low-grade inflammation should potentially be tested to prevent the development of CAN. Significance: Cardiac reflex responses should be monitored closely to diagnose and classify severity levels of CAN accurately. Standard clinical biochemical analytes, such as glycaemic level, lipidic level, or kidney function were not included in the ten most important features. Beat-to-beat measures accounted for approximately 60% of the features in the ensembled data.

KW - Decision tree

KW - Diabetes

KW - Ensembling

KW - Machine learning

KW - Random forest

KW - Support vector machine

U2 - 10.1016/j.clinph.2023.06.011

DO - 10.1016/j.clinph.2023.06.011

M3 - Journal article

C2 - 37442682

AN - SCOPUS:85164710349

VL - 154

SP - 200

EP - 208

JO - Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control

JF - Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control

SN - 1388-2457

ER -