Predicting diabetes in healthy population through machine learning

Hasan Abbas, Lejla Alic, Marelyn Rios, Muhammad Abdul-Ghani, Khalid Qaraqe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages567-570
Number of pages4
ISBN (Electronic)9781728122861
DOIs
Publication statusPublished - 1 Jun 2019
Event32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Cordoba, Spain
Duration: 5 Jun 20197 Jun 2019

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2019-June
ISSN (Print)1063-7125

Conference

Conference32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
CountrySpain
CityCordoba
Period5/6/197/6/19

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Keywords

  • Disease Prediction
  • Support vector machine
  • Type 2 diabetes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

Cite this

Abbas, H., Alic, L., Rios, M., Abdul-Ghani, M., & Qaraqe, K. (2019). Predicting diabetes in healthy population through machine learning. In Proceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019 (pp. 567-570). [8787404] (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2019.00117