Alhassan, Zakhriya and Budgen, David and Alessa, Ali and Alshammari, Riyad and Daghstani, Tahini and Al Moubayed, Noura (2019) 'Collaborative denoising autoencoder for high glycated haemoglobin prediction.', in Artificial neural networks and machine learning – ICANN 2019; 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019 ; proceedings. Cham: Springer, pp. 338-350. Lecture notes in computer science. (11731).
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.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1007/978-3-030-30493-5_34|
|Publisher 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|
|Date accepted:||25 June 2019|
|Date deposited:||02 July 2019|
|Date of first online publication:||09 September 2019|
|Date first made open access:||13 November 2019|
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