Predicting diabetes in healthy population through machine learning

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

doi:10.1109/CBMS.2019.00117

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 proceeding › Conference contribution › peer-review

36 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	567-570
Number of pages	4
ISBN (Electronic)	9781728122861
DOIs	https://doi.org/10.1109/CBMS.2019.00117
Publication status	Published - Jun 2019
Externally published	Yes
Event	32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Cordoba, Spain Duration: 5 Jun 2019 → 7 Jun 2019

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2019-June
ISSN (Print)	1063-7125

Conference

Conference	32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
Country/Territory	Spain
City	Cordoba
Period	5/06/19 → 7/06/19

Keywords

Disease Prediction
Support vector machine
Type 2 diabetes

Access to Document

10.1109/CBMS.2019.00117

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). Article 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

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title = "Predicting diabetes in healthy population through machine learning",

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.",

keywords = "Disease Prediction, Support vector machine, Type 2 diabetes",

author = "Hasan Abbas and Lejla Alic and Marelyn Rios and Muhammad Abdul-Ghani and Khalid Qaraqe",

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year = "2019",

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language = "English",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

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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., 8787404, Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. 567-570, 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019, Cordoba, Spain, 5/06/19. https://doi.org/10.1109/CBMS.2019.00117

Predicting diabetes in healthy population through machine learning. / Abbas, Hasan; Alic, Lejla; Rios, Marelyn et al.
Proceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 567-570 8787404 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting diabetes in healthy population through machine learning

AU - Abbas, Hasan

AU - Alic, Lejla

AU - Rios, Marelyn

AU - Abdul-Ghani, Muhammad

AU - Qaraqe, Khalid

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Disease Prediction

KW - Support vector machine

KW - Type 2 diabetes

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DO - 10.1109/CBMS.2019.00117

M3 - Conference contribution

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T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems

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EP - 570

BT - Proceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

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

Predicting diabetes in healthy population through machine learning

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