Predicting Diabetes in Healthy Population through Machine Learning

Abbas, H. , Alic, L., Rios, M., Abdul-Ghani, M. and Qaraqe, K. (2019) Predicting Diabetes in Healthy Population through Machine Learning. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 05-07 Jun 2019, pp. 567-570. ISBN 9781728122861 (doi: 10.1109/cbms.2019.00117)

Abstract

In this paper, we revisit the data of the San Antonio Heart Study, and employ machine learning to predict the future development of type-2 diabetes. To build the prediction model, we use the support vector machines and ten features that are wellknown in the literature as strong predictors of future diabetes. Due to the unbalanced nature of the dataset in terms of the class labels, we use 10-fold cross-validation to train the model and a hold-out set to validate it. The results of this study show a validation accuracy of 84.1% with a recall rate of 81.1% averaged over 100 iterations. The outcomes of this study can help in identifying the population that is at high risk of developing type-2 diabetes in the future.

• Item Type: Conference Proceedings
• Additional Information: This publication was made possible by NPRP grant number NPRP 10-1231-160071 from the Qatar National Research Fund (a member of Qatar Foundation).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbas, Dr Hasan
• Authors: Abbas, H., Alic, L., Rios, M., Abdul-Ghani, M., and Qaraqe, K.
• College/School: College of Science and Engineering > School of Engineering
• ISSN: 2372-9198
• ISBN: 9781728122861
• Published Online: 05 August 2019
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS): 567-570
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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