TY - GEN
T1 - Data Mining Techniques for Prediction of Type 2 Diabetes leading to Cardiovascular Disease
AU - Islam, Md Shafiqul
AU - Belhaouari, Samir Brahim
AU - Abdul-Ghani, Muhammad
AU - Qaraqe, Marwa K.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/14
Y1 - 2021/6/14
N2 - Prevention or late onset of a disease progression can be accomplished if a data-mining technique can identify a person who is at a greater risk of developing the disease in a later stage. This study aimed to extract and find the biomarkers responsible for the progression of diabetes mellitus (DM) leading to cardiovascular disease (CVD), followed by applying data-driven techniques for type 2 diabetes (T2D) and CVD prediction in advance. The proposed approach comprises novel feature extraction and selection, applying ensembling and stacking of three different data mining techniques, namely, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) models. The developed framework has been evaluated using oral glucose tolerant test (OGTT) data sourced from the San Antonio Heart Study. The model achieved 92.54% prediction accuracy in differentiating healthy patients from those who developed T2D leading to CVD.
AB - Prevention or late onset of a disease progression can be accomplished if a data-mining technique can identify a person who is at a greater risk of developing the disease in a later stage. This study aimed to extract and find the biomarkers responsible for the progression of diabetes mellitus (DM) leading to cardiovascular disease (CVD), followed by applying data-driven techniques for type 2 diabetes (T2D) and CVD prediction in advance. The proposed approach comprises novel feature extraction and selection, applying ensembling and stacking of three different data mining techniques, namely, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) models. The developed framework has been evaluated using oral glucose tolerant test (OGTT) data sourced from the San Antonio Heart Study. The model achieved 92.54% prediction accuracy in differentiating healthy patients from those who developed T2D leading to CVD.
KW - Cardiovascular Disease Prediction
KW - Data Mining
KW - Diabetes Management
KW - Feature Extraction
KW - OGTT
KW - SASH Study
UR - http://www.scopus.com/inward/record.url?scp=85119858973&partnerID=8YFLogxK
U2 - 10.1109/WF-IoT51360.2021.9596035
DO - 10.1109/WF-IoT51360.2021.9596035
M3 - Conference contribution
AN - SCOPUS:85119858973
T3 - 7th IEEE World Forum on Internet of Things, WF-IoT 2021
SP - 321
EP - 325
BT - 7th IEEE World Forum on Internet of Things, WF-IoT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE World Forum on Internet of Things, WF-IoT 2021
Y2 - 14 June 2021 through 31 July 2021
ER -