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

Magnetic Detection and Imaging

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

35 Citations (Scopus)

22 Downloads (Pure)

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	2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019
Publisher	IEEE
Pages	567-570
Number of pages	4
ISBN (Electronic)	9781728122861
DOIs	https://doi.org/10.1109/CBMS.2019.00117
Publication status	Published - 5 Aug 2019
Event	32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Instituto Maimónides de Investigación Biomédica de Córdoba, Cordoba, Spain Duration: 5 Jun 2019 → 7 Jun 2019 Conference number: 32

Conference

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

Keywords

Disease Prediction
Support vector machine
Type 2 diabetes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CBMS.2019.00117

Cite this

@inproceedings{3784fcda54924d148303d9add6430d30,

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",

year = "2019",

month = aug,

day = "5",

doi = "10.1109/CBMS.2019.00117",

language = "English",

pages = "567--570",

booktitle = "2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019",

publisher = "IEEE",

address = "United States",

note = "32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019, CBMS 2019 ; Conference date: 05-06-2019 Through 07-06-2019",

}

Abbas, H, Alic, L, Rios, M, Abdul-Ghani, M & Qaraqe, K 2019, Predicting diabetes in healthy population through machine learning. in 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019., 8787404, IEEE, 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

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

N1 - Conference code: 32

PY - 2019/8/5

Y1 - 2019/8/5

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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U2 - 10.1109/CBMS.2019.00117

DO - 10.1109/CBMS.2019.00117

M3 - Conference contribution

AN - SCOPUS:85070958362

SP - 567

EP - 570

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

PB - IEEE

T2 - 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019

Y2 - 5 June 2019 through 7 June 2019

ER -

Predicting diabetes in healthy population through machine learning

Abstract

Conference

Keywords

UN SDGs

Access to Document

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