Key result
A remodelled Convolutional Neural Network architecture achieved 95.42% efficiency in classifying between groups with and without Cardiac Autonomic Neuropathy.
Why the study?
Cardiac Autonomic Neuropathy is a severe complication in patients with long-duration diabetes, motivating predictive mechanisms designed for more reliable diagnostics using ECG physiology and remodelled CNN architecture.
Does a remodelled CNN architecture accurately classify Cardiac Autonomic Neuropathy (CAN) using ECG physiology in diabetic patients?
Does a remodelled CNN architecture accurately classify Cardiac Autonomic Neuropathy (CAN) using ECG physiology in diabetic patients?
Answers are grounded in this paper and cite their sources.
A remodelled CNN architecture analyzing ECG physiology achieved high efficiency (95.42%) in diagnosing Cardiac Autonomic Neuropathy in diabetic patients.
Convolutional Neural Networks (CNN) are widely used as prediction models in medical diagnosis in the recent research. Remodelling the CNN architecture to make it more reliable for classification is the core of each finding. Cardiac Autonomic Neuropathy (CAN) is a severity amongst the diabetic population, who are subject to diabetes for long duration. The aim of this work is to provide a predictive mechanism that is designed for more reliable diagnostics by studying the ECG physiology and enhancing the diagnostics by artificial technique, like using a remodelled CNN architecture. Results of CNN show 95.42 % efficiency in classifying between groups of CAN+ and CAN-groups..
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Sharanya et al. (2021) studied Cardiac Autonomic Neuropathy (CAN). Remodelled Convolutional Neural Network (CNN) architecture was evaluated on Classification efficiency between CAN+ and CAN- groups. A remodelled Convolutional Neural Network architecture achieved 95.42% efficiency in classifying between groups with and without Cardiac Autonomic Neuropathy.
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