Machine learning-based prediction of diabetes risk by combining exposome and electrocardiographic predictors

Zeinab Shahbazi; Marina Camacho; Esmeralda Ruiz; Angelica Atehortua; Karim Lekadir

doi:10.1117/12.2670078

6 March 2023 Machine learning-based prediction of diabetes risk by combining exposome and electrocardiographic predictors

Zeinab Shahbazi, Marina Camacho, Esmeralda Ruiz, Angelica Atehortua, Karim Lekadir

Author Affiliations +

Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125670W (2023) https://doi.org/10.1117/12.2670078
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

Diabetes is a high-burden non-communicable disease affecting more than 532 million people worldwide and resulting in a range of life-threatening comorbidities. Pre-identifying high-risk individuals and applying preventive actions will likely reduce the prevalence and health consequences of diabetes. Under this context, we developed and evaluated the first predictive model of diabetes risk that combines both electrocardiography (ECG) and exposome predictors. A comprehensive list of ECG signals and exposome variables were extracted from the UK Biobank, then used to build and compare a set of machine learning models for diabetes risk prediction. Random Forest combining ECGs and exposome variables achieved an 0.82 ± 0.03 AUC when predicting diabetes risk. This integrative model outperformed separate models based on exposome factors or ECG signals alone. These preliminary results indicate the potential of low-cost machine learning models trained from ECG and exposome data to predict diabetes years before its onset.

Citation Download Citation

Zeinab Shahbazi, Marina Camacho, Esmeralda Ruiz, Angelica Atehortua, and Karim Lekadir "Machine learning-based prediction of diabetes risk by combining exposome and electrocardiographic predictors", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125670W (6 March 2023); https://doi.org/10.1117/12.2670078

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Electrocardiography

Machine learning

Data modeling

Performance modeling

Education and training

Random forests

Depolarization

Show All Keywords

Keywords/Phrases

Search In:

Publication Years