Open AccessA model for early diagnosis of Cardiac Autonomic Neuropathy (CAN)
Author(s) -
S. Sharanya,
Parupally Sridhar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2089/1/012053
Subject(s) - convolutional neural network , computer science , mechanism (biology) , artificial intelligence , population , diabetes mellitus , architecture , machine learning , medicine , art , visual arts , philosophy , environmental health , epistemology , endocrinology
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..