TY - GEN
T1 - The prediction of diabetes development
T2 - 5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020
AU - Islam, Md Shafiqul
AU - Qaraqe, Marwa K.
AU - Abbas, Hasan T.
AU - Erraguntla, Madhav
AU - Abdul-Ghani, Muhammad
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/27
Y1 - 2020/10/27
N2 - The development of diabetes occurs due to elevated glucose levels in the bloodstream. Prevention of diabetes or the delayed onset of diabetes is crucial. It can be achieved if there exists a screening process that can accurately identify individuals who are at a higher risk of developing diabetes in the future. Although there are many works employing machine learning techniques in medical diagnostics, there is little work done regarding the long term prediction of disease, type 2 diabetes in particular. In this study, we propose a machine learning framework consists of finding the best features that are highly correlated with the future development of diabetes, followed by developing diabetes prediction models. The proposed models are evaluated using data from a longitudinal clinical study known as the San Antonio Heart Study. Our approach has managed to achieve a long-term prediction accuracy of 81.01%, a specificity of 81.2%, a sensitivity of 79.5%, and an AUC score of 87.1%.
AB - The development of diabetes occurs due to elevated glucose levels in the bloodstream. Prevention of diabetes or the delayed onset of diabetes is crucial. It can be achieved if there exists a screening process that can accurately identify individuals who are at a higher risk of developing diabetes in the future. Although there are many works employing machine learning techniques in medical diagnostics, there is little work done regarding the long term prediction of disease, type 2 diabetes in particular. In this study, we propose a machine learning framework consists of finding the best features that are highly correlated with the future development of diabetes, followed by developing diabetes prediction models. The proposed models are evaluated using data from a longitudinal clinical study known as the San Antonio Heart Study. Our approach has managed to achieve a long-term prediction accuracy of 81.01%, a specificity of 81.2%, a sensitivity of 79.5%, and an AUC score of 87.1%.
KW - Diabetes Prediction
KW - Ensemble of Classifiers
KW - Feature Selection
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85105253902&partnerID=8YFLogxK
U2 - 10.1109/MECBME47393.2020.9292043
DO - 10.1109/MECBME47393.2020.9292043
M3 - Conference contribution
AN - SCOPUS:85105253902
T3 - Middle East Conference on Biomedical Engineering, MECBME
BT - 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering, MECBME 2020
PB - IEEE Computer Society
Y2 - 27 October 2020 through 29 October 2020
ER -