Features characterising cardiac autonomic neuropathy in diabetes using ensembled classification

Rasmus Bach Nedergaard*, Matthew Scott, Anne Marie Wegeberg, Tina Okdahl, Joachim Størling, Birgitte Brock, Asbjørn Mohr Drewes, Christina Brock

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

3 Citationer (Scopus)
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Abstract

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.
OriginalsprogEngelsk
TidsskriftClinical Neurophysiology
Vol/bind154
Sider (fra-til)200-208
Antal sider9
ISSN1388-2457
DOI
StatusUdgivet - okt. 2023

Bibliografisk note

Funding Information:
No specific funding was used for this publication. The corresponding author, Rasmus Bach Nedergaard, has full access to all data and the right to publish such data. All authors participated in a meaningful way in the preparation of the manuscript.

Publisher Copyright:
© 2023 International Federation of Clinical Neurophysiology

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