Diabetes detection using deep learning techniques with oversampling and
  feature augmentation
María Teresa García-Ordás Carmen Benavides José Alberto Benítez-Andrades Héctor Alaiz-Moretón Isaías García-Rodríguez
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.