Publication detailsAlhassan, Zakhriya, Budgen, David, Alessa, Ali, Alshammari, Riyad, Daghstani, Tahini & Al Moubayed, Noura (2019), Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction, in Tetko, Igor V. Kůrková, Věra Karpov, Pavel & Theis, Fabian eds, Lecture Notes in Computer Science 11731: 28th International Conference on Artificial Neural Networks, ICANN2019. Munich, Germany, Springer, Cham, 338-350.
- Publication type: Conference Paper
- ISSN/ISBN: 9783030304928, 9783030304935
- DOI: 10.1007/978-3-030-30493-5_34
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.