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 -