The present study
aimed at investigating machine learning methods for automated detection of
severe diabetic neuropathy. Severe diabetic neuropathy represents a significant
neurological problem in diabetes as it requires urgent intervention to reduce
the risk of sudden cardiac death.
Automated
detection provides a tool that can be applied to clinical data and for
identifying comorbidities that can trigger diagnosis and treatment.We applied multi
scale Allen factor to determine heart rate variability, a marker for
diabetic neuropathy from ECG recordings as features to be used for the machine
learning methods and automated detection.
The major
innovation of this work is the introduction of a new Graph-Based Machine
Learning System (GBMLS).
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