Machine Learning Methods for Automated Detection of Severe Diabetic Neuropathy

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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Machine Learning Methods for Automated Detection of Severe Diabetic Neuropathy

Diabetic neuropathy associated complications, which affects all major organs of the body, are common in Type 1 and Type 2 diabetes. Cardiac diabetic neuropathy is characterised by damage to nerves regulating the heart rate and any changes in the capacity of these nerves to modulate heart rate leads to changes to heart rate variability (HRV).

Its prevalence lies between 20% and 60% in patients with diabetes, with a mortality that is approximately five times higher.Testing for cardiac autonomic neuropathy (CAN) in people with diabetes was traditionally based on five Ewing cardiac reflex tests that constitute the gold standard.

Recent research has been investigating the efficacy of alternative diagnostic tests, using ECG attributes to address shortcomings of the Ewing battery as a number of the Ewing tests included in the Ewing battery are often counter-indicated for patients with cardiorespiratory comorbidity, frail or severely obese patients.

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Machine Learning Methods for Automated Detection of Severe Diabetic Neuropathy

Diabetic neuropathy associated complications, which affects all major organs of the body, are common in Type 1 and Type 2 diabetes. Cardiac diabetic neuropathy is characterised by damage to nerves regulating the heart rate and any changes in the capacity of these nerves to modulate heart rate leads to changes to heart rate variability (HRV).

Its prevalence lies between 20% and 60% in patients with diabetes, with a mortality that is approximately five times higher.Testing for cardiac autonomic neuropathy (CAN) in people with diabetes was traditionally based on five Ewing cardiac reflex tests that constitute the gold standard.

Recent research has been investigating the efficacy of alternative diagnostic tests, using ECG attributes to address shortcomings of the Ewing battery as a number of the Ewing tests included in the Ewing battery are often counter-indicated for patients with cardiorespiratory comorbidity, frail or severely obese patients.

PDF LINK