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

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, October). Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models. Presented at 27th International Conference on Artificial Neural Networks (ICANN)., Rhodes, Greece

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 27th International Conference on Artificial Neural Networks (ICANN).
• 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
• Print ISSN: 0302-9743
• 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