TY - GEN
T1 - Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk
AU - Wang, Hanyin
AU - Li, Yikuan
AU - Ning, Hongyan
AU - Wilkins, John T
AU - Lloyd-Jones, Donald M
AU - Luo, Yuan
N1 - Funding Information:
This work was supported by NIH Grant 1R21LM012618. We thank Ying Liu at UT Dallas for helpful comment.
Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press.
PY - 2019/8/21
Y1 - 2019/8/21
N2 - Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved prognosis. Health disparities, manifested as socio-behavioral factors, are believed to have multiple effects throughout life with great complexity. Multiple studies investigated lifetime cardiovascular-related mortality risk prediction focusing on subjects' pathophysiology and intervention profiles. In this study, we applied machine learning algorithms and focused on integrating socio-behavioral factors to pathophysiology and intervention profiles to predict cardiovascular-related mortality risk. Our results showed that multiple machine learning algorithms can predict risk with reasonable accuracy, using mixed types of features. Particularly, socio-behavioral factors contributed significantly to the improved accuracy of mortality risk prediction. Feature analysis identified the odds ratio of socio-behavioral factors for cardiovascular-related mortality and offered potential insights on how they impact subjects' long-term outcomes. Our results call for further investigation of this important topic.
AB - Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved prognosis. Health disparities, manifested as socio-behavioral factors, are believed to have multiple effects throughout life with great complexity. Multiple studies investigated lifetime cardiovascular-related mortality risk prediction focusing on subjects' pathophysiology and intervention profiles. In this study, we applied machine learning algorithms and focused on integrating socio-behavioral factors to pathophysiology and intervention profiles to predict cardiovascular-related mortality risk. Our results showed that multiple machine learning algorithms can predict risk with reasonable accuracy, using mixed types of features. Particularly, socio-behavioral factors contributed significantly to the improved accuracy of mortality risk prediction. Feature analysis identified the odds ratio of socio-behavioral factors for cardiovascular-related mortality and offered potential insights on how they impact subjects' long-term outcomes. Our results call for further investigation of this important topic.
KW - Cardiovascular Diseases
KW - Machine Learning
KW - Risk Factors
UR - http://www.scopus.com/inward/record.url?scp=85071459189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071459189&partnerID=8YFLogxK
U2 - 10.3233/SHTI190258
DO - 10.3233/SHTI190258
M3 - Conference contribution
C2 - 31437960
AN - SCOPUS:85071459189
T3 - Studies in Health Technology and Informatics
SP - 433
EP - 437
BT - MEDINFO 2019
A2 - Seroussi, Brigitte
A2 - Ohno-Machado, Lucila
A2 - Ohno-Machado, Lucila
A2 - Seroussi, Brigitte
PB - IOS Press
T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019
Y2 - 25 August 2019 through 30 August 2019
ER -
