TY - GEN
T1 - Robust Detection of Cardiac Disease Using Machine Learning Algorithms
AU - Domyati, Anas
AU - Memon, Qurban A.
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/8/19
Y1 - 2022/8/19
N2 - The contribution of the current work is to facilitate diagnose the heart disease based on contemporary machine learning algorithms. The performances of the classifiers are tested on feature spaces selected through various feature selection algorithms. The relief feature selection algorithm was selected for vital and more correlated features. The models were trained and tested on the Cleveland (S1) and Hungarian (S2) heart disease datasets. Several performance measures such as accuracy, sensitivity, specificity, and F1 score are used to observe the effectiveness of the selected models. It is found out that SVM and random forest achieved very promising results with both full feature space and selected feature space, specifically with relief feature selection algorithm.
AB - The contribution of the current work is to facilitate diagnose the heart disease based on contemporary machine learning algorithms. The performances of the classifiers are tested on feature spaces selected through various feature selection algorithms. The relief feature selection algorithm was selected for vital and more correlated features. The models were trained and tested on the Cleveland (S1) and Hungarian (S2) heart disease datasets. Several performance measures such as accuracy, sensitivity, specificity, and F1 score are used to observe the effectiveness of the selected models. It is found out that SVM and random forest achieved very promising results with both full feature space and selected feature space, specifically with relief feature selection algorithm.
KW - Random Forest
KW - Relief feature selection
KW - Robust detection
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85142621822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142621822&partnerID=8YFLogxK
U2 - 10.1145/3561613.3561622
DO - 10.1145/3561613.3561622
M3 - Conference contribution
AN - SCOPUS:85142621822
T3 - ACM International Conference Proceeding Series
SP - 52
EP - 55
BT - ICCCV 2022 - Proceedings of the 5th International Conference on Control and Computer Vision
PB - Association for Computing Machinery
T2 - 5th International Conference on Control and Computer Vision, ICCCV 2022
Y2 - 19 August 2022 through 21 August 2022
ER -