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 - Funding Information:
ACKNOWLEDGEMENTS This publication was made possible by NPRP grant number NPRP 10-1231-160071 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Publisher Copyright:
© 2019 IEEE.
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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U2 - 10.1109/CBMS.2019.00117
DO - 10.1109/CBMS.2019.00117
M3 - Conference contribution
AN - SCOPUS:85070958362
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 567
EP - 570
BT - Proceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
Y2 - 5 June 2019 through 7 June 2019
ER -