TY - CHAP
T1 - Comparison of Supervised Learning Models for the Prediction of Coronary Artery Disease
AU - Vasquez-Gonzaga, Hillary
AU - Gutierrez-Cardenas, Juan
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021/7/21
Y1 - 2021/7/21
N2 - Cardiovascular diseases and Coronary Artery Disease (CAD) are the leading causes of mortality among people of different ages and conditions. The use of different and not so invasive biomarkers to detect these types of diseases joined with Machine Learning techniques seems promising for early detection of these illnesses. In the present work, we have used the Sani Z-Alizadeh dataset, which comprises a set of different medical features extracted with not invasive methods and used with different machine learning models. The comparisons performed showed that the best results were using a complete set and a subset of features as input for the Random Forest and XGBoost algorithms. Considering the results obtained, we believe that using a complete set of features gives insights that the features should also be analyzed by considering the medical advances and findings of how these markers influence a CAD disease's presence.
AB - Cardiovascular diseases and Coronary Artery Disease (CAD) are the leading causes of mortality among people of different ages and conditions. The use of different and not so invasive biomarkers to detect these types of diseases joined with Machine Learning techniques seems promising for early detection of these illnesses. In the present work, we have used the Sani Z-Alizadeh dataset, which comprises a set of different medical features extracted with not invasive methods and used with different machine learning models. The comparisons performed showed that the best results were using a complete set and a subset of features as input for the Random Forest and XGBoost algorithms. Considering the results obtained, we believe that using a complete set of features gives insights that the features should also be analyzed by considering the medical advances and findings of how these markers influence a CAD disease's presence.
UR - https://hdl.handle.net/20.500.12724/17543
UR - https://www.scopus.com/pages/publications/85119206955
UR - https://www.mendeley.com/catalogue/14649870-09dd-3df6-ab76-1811f19db7a7/
U2 - 10.1145/3480433.3480451
DO - 10.1145/3480433.3480451
M3 - Capítulo
AN - SCOPUS:85119206955
SN - 9781450384148
T3 - ACM International Conference Proceeding Series
SP - 98
EP - 103
BT - ACM International Conference Proceeding Series
PB - Association for Computing Machinery
T2 - 5th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2021
Y2 - 23 July 2021 through 25 July 2021
ER -