Zakhriya Alhassan
Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction
Alhassan, Zakhriya; Budgen, David; Alessa, Ali; Alshammari, Riyad; Daghstani, Tahini; Al Moubayed, Noura
Authors
Professor David Budgen david.budgen@durham.ac.uk
Emeritus Professor
Ali Alessa
Riyad Alshammari
Tahini Daghstani
Dr Noura Al Moubayed noura.al-moubayed@durham.ac.uk
Associate Professor
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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