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Diabetes detection using deep learning techniques with oversampling and feature augmentation.

María Teresa García-Ordás1, Carmen Benavides2, José Alberto Benítez-Andrades2

  • 1SECOMUCI Research Groups, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, León C.P. 24071, Spain.

Computer Methods and Programs in Biomedicine
|February 25, 2021
PubMed
Summary

This study introduces a deep learning pipeline for early diabetes detection. The method achieved 92.31% accuracy, outperforming existing approaches for diabetic patient prediction.

Keywords:
Deep learningDetectionDiabetesOversamplingSparse autoencoderVariational autoencoder

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Diabetes mellitus is a growing global health concern with significant mortality rates.
  • Late diagnosis of diabetes leads to severe complications and increased fatalities.
  • Early detection methods are crucial for managing diabetes and improving patient outcomes.

Purpose of the Study:

  • To develop and evaluate a deep learning pipeline for the early prediction of diabetes.
  • To enhance diagnostic accuracy through advanced data and feature augmentation techniques.

Main Methods:

  • A deep learning pipeline integrating variational autoencoder (VAE) for data augmentation and sparse autoencoder (SAE) for feature augmentation.
  • Utilized a convolutional neural network (CNN) for classification on the Pima Indians Diabetes Database.
  • Evaluated patient data including pregnancies, glucose, insulin, blood pressure, and age.

Main Results:

  • Achieved a classification accuracy of 92.31% using the CNN classifier trained with SAE feature augmentation.
  • Demonstrated a 3.17% improvement in accuracy compared to current state-of-the-art methods.
  • The proposed pipeline showed high efficacy on a balanced dataset.

Conclusions:

  • A comprehensive deep learning pipeline offers a promising approach for diabetes detection.
  • The integrated deep learning methods significantly outperform existing state-of-the-art techniques in diabetes prediction.
  • This methodology highlights the potential of AI in improving early diagnosis and management of chronic diseases like diabetes.