Skip to main content

Research Repository

Advanced Search

Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models

Alhassan, Zakhriya; McGough, Stephen; Alshammari, Riyad; Daghstani, Tahini; Budgen, David; Al Moubayed, Noura

Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models Thumbnail


Authors

Zakhriya Alhassan

Stephen McGough

Riyad Alshammari

Tahini Daghstani



Contributors

Věra Kůrková
Editor

Yannis Manolopoulos
Editor

Barbara Hammer
Editor

Lazaros Iliadis
Editor

Ilias Maglogiannis
Editor

Abstract

Clinical data is usually observed and recorded at irregular intervals and includes: evaluations, treatments, vital sign and lab test results. These provide an invaluable source of information to help diagnose and understand medical conditions. In this work, we introduce the largest patient records dataset in diabetes research: King Abdullah International Research Centre Diabetes (KAIMRCD) which includes over 14k patient data. KAIMRCD contains detailed information about the patient’s visit and have been labelled against T2DM by clinicians. The data is processed as time series and then investigated using temporal predictive Deep Learning models with the goal of diagnosing Type 2 Diabetes Mellitus (T2DM). Long Short-Term Memory (LSTM) and Gated-Recurrent Unit (GRU) are trained on KAIMRCD and are demonstrated here to outperform classical machine learning approaches in the literature with over 97% accuracy.

Citation

Alhassan, Z., McGough, S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III (468-478). https://doi.org/10.1007/978-3-030-01424-7_46

Conference Name 27th International Conference on Artificial Neural Networks (ICANN).
Conference Location Rhodes, Greece
Start Date Oct 4, 2018
End Date Oct 7, 2018
Acceptance Date Jul 12, 2018
Online Publication Date Sep 27, 2018
Publication Date Oct 1, 2018
Deposit Date Aug 9, 2018
Publicly Available Date Sep 27, 2019
Volume III
Pages 468-478
Series Title Lecture notes in computer science
Series Number 11141
Series ISSN 0941-0643,1433-3058
Book Title Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III.
ISBN 9783030014230
DOI https://doi.org/10.1007/978-3-030-01424-7_46
Public URL https://durham-repository.worktribe.com/output/1144864

Files

Accepted Conference Proceeding (553 Kb)
PDF

Copyright Statement
This is a post-peer-review, pre-copyedit version of an article published in Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01424-7_46







You might also like



Downloadable Citations