Features characterising cardiac autonomic neuropathy in diabetes using ensembled classification

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

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.

Original languageEnglish
Book seriesClinical Neurophysiology
Volume154
Pages (from-to)200-208
ISSN1388-2457
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 International Federation of Clinical Neurophysiology

    Research areas

  • Decision tree, Diabetes, Ensembling, Machine learning, Random forest, Support vector machine

ID: 363282273