TY - GEN
T1 - Cataloguing of Coronary Heart Malady Using Machine Learning Algorithms
AU - Cenitta, D.
AU - Vijaya Arjunan, R.
AU - Prema, K. V.
AU - Vidya Bai, G.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Cardiovascular Diseases (CVDs) are the leading cause of death worldwide, according to the World Health Organization: more people die each year from CVDs than from any other cause. CVDs claimed the lives of 17.9 million people worldwide in 2016, accounting for 31% of all deaths. Heart attacks and strokes account for 85 percent of these deaths. The proposed work is about the application of ML (Machine Learning) techniques for the classification and prediction of heart diseases (HD). The scope of the present study includes investigations of main determinants to remove irrelevant and redundant features using the feature selection technique, compare different Machine Learning classification algorithms on the heart disease data set and to identify better performance-based classification technique for heart disease classification.
AB - Cardiovascular Diseases (CVDs) are the leading cause of death worldwide, according to the World Health Organization: more people die each year from CVDs than from any other cause. CVDs claimed the lives of 17.9 million people worldwide in 2016, accounting for 31% of all deaths. Heart attacks and strokes account for 85 percent of these deaths. The proposed work is about the application of ML (Machine Learning) techniques for the classification and prediction of heart diseases (HD). The scope of the present study includes investigations of main determinants to remove irrelevant and redundant features using the feature selection technique, compare different Machine Learning classification algorithms on the heart disease data set and to identify better performance-based classification technique for heart disease classification.
UR - http://www.scopus.com/inward/record.url?scp=85123405929&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123405929&partnerID=8YFLogxK
U2 - 10.1109/ICECCT52121.2021.9616913
DO - 10.1109/ICECCT52121.2021.9616913
M3 - Conference contribution
AN - SCOPUS:85123405929
T3 - 2021 4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021
BT - 2021 4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021
Y2 - 15 September 2021 through 17 September 2021
ER -
