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Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction

Alhassan, Zakhriya; Budgen, David; Alessa, Ali; Alshammari, Riyad; Daghstani, Tahini; Al Moubayed, Noura

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Authors

Zakhriya Alhassan

Ali Alessa

Riyad Alshammari

Tahini Daghstani



Contributors

Igor V. Tetko
Editor

Věra Kůrková
Editor

Pavel Karpov
Editor

Fabian Theis
Editor

Abstract

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Type-2 Diabetes Mellitus (T2DM). This will save healthcare providers a major cost associated with the administration and assessment of clinical tests for HbA1c. A novel collaborative denoising autoencoder framework is used to address this challenge. The framework builds an independent denoising autoencoder model for the high and low HbA1c level, which extracts feature representations in the latent space. A baseline model using just three features: patient age together with triglycerides and glucose level achieves 76% F1-score with an SVM classifier. The collaborative denoising autoencoder uses 78 features and can predict HbA1c level with 81% F1-score.

Citation

Alhassan, Z., Budgen, D., Alessa, A., Alshammari, R., Daghstani, T., & Al Moubayed, N. (2019). Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction. In I. V. Tetko, V. Kůrková, P. Karpov, & F. Theis (Eds.), Artificial neural networks and machine learning – ICANN 2019; 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019 ; proceedings (338-350). https://doi.org/10.1007/978-3-030-30493-5_34

Conference Name 28th International Conference on Artificial Neural Networks (ICANN2019)
Conference Location Munich, Germany
Start Date Sep 17, 2019
End Date Sep 19, 2019
Acceptance Date Jun 25, 2019
Online Publication Date Sep 9, 2019
Publication Date Jan 1, 2019
Deposit Date Jul 2, 2019
Publicly Available Date Mar 29, 2024
Publisher Springer Verlag
Pages 338-350
Series Title Lecture notes in computer science
Series Number 11731
Book Title Artificial neural networks and machine learning – ICANN 2019; 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019 ; proceedings.
ISBN 9783030304928
DOI https://doi.org/10.1007/978-3-030-30493-5_34
Public URL https://durham-repository.worktribe.com/output/1142524

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Copyright Statement
This is a post-peer-review, pre-copyedit version of an chapter published in Artificial neural networks and machine learning – ICANN 2019; 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019 ; proceedings. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-30493-5_34





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