Diabetes detection using deep learning techniques with oversampling and feature augmentation

•A deep learning model has been built to address the prediction of diabetes.•Variational auto encoder was trained for sample data augmentation.•Sparse auto encoder was trained for feature augmentation.•Results obtained demonstrate the powerful of the model using convolutional layers•A 92.31% of accu...

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Vydané v:Computer methods and programs in biomedicine Ročník 202; s. 105968
Hlavní autori: García-Ordás, María Teresa, Benavides, Carmen, Benítez-Andrades, José Alberto, Alaiz-Moretón, Héctor, García-Rodríguez, Isaías
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Ireland Elsevier B.V 01.04.2021
Predmet:
Databases, Factual
Deep Learning
Detection
Diabetes
Diabetes Mellitus - diagnosis
Humans
Neural Networks, Computer
Oversampling
Sparse autoencoder
Variational autoencoder
Deep learning
Variational autoencoder
Sparse autoencoder
Diabetes
Oversampling
Detection
ISSN:0169-2607, 1872-7565, 1872-7565
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Shrnutí:•A deep learning model has been built to address the prediction of diabetes.•Variational auto encoder was trained for sample data augmentation.•Sparse auto encoder was trained for feature augmentation.•Results obtained demonstrate the powerful of the model using convolutional layers•A 92.31% of accuracy was obtained outperforming the state of the art techniques. Background and objective: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of their health status early enough. Late diagnosis brings about numerous health problems and a large number of deaths each year so the development of methods for the early diagnosis of this pathology is essential. Methods: In this paper, a pipeline based on deep learning techniques is proposed to predict diabetic people. It includes data augmentation using a variational autoencoder (VAE), feature augmentation using an sparse autoencoder (SAE) and a convolutional neural network for classification. Pima Indians Diabetes Database, which takes into account information on the patients such as the number of pregnancies, glucose or insulin level, blood pressure or age, has been evaluated. Results: A 92.31% of accuracy was obtained when CNN classifier is trained jointly the SAE for featuring augmentation over a well balanced dataset. This means an increment of 3.17% of accuracy with respect the state-of-the-art. Conclusions: Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.
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ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2021.105968