Diabetes detection using deep learning algorithms

Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which...

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Vydáno v:ICT express Ročník 4; číslo 4; s. 243 - 246
Hlavní autoři: G., Swapna, R., Vinayakumar, K.P., Soman
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier 01.12.2018
한국통신학회
Témata:
전자/정보통신공학
ISSN:2405-9595, 2405-9595
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Shrnutí:Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning architectures. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%. Keywords: Deep learning, Diabetes, Heart rate variability, ECG, CNN, LSTM
ISSN:2405-9595
2405-9595
DOI:10.1016/j.icte.2018.10.005