TY - GEN
T1 - A Relative Analysis on the Spotting of Cardiovascular Disease Employing Machine Learning Techniques
AU - Pavan Kumar, S. P.
AU - Samiha, C. M.
AU - Anusha, K. S.
AU - Gururaj, H. L.
AU - Krishnamoorthy, Ramkumar
AU - Robert, Nismon Rio
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Heart is one of the significant segments in the human body since it powers blood to the all the pieces of the body. Blood courses through the vein. Cardiovascular sickness is corresponded with the blockage of vein. The sign of heart sickness depends whereupon condition is impacting an individual. The term coronary illness is ordinarily utilized instead of cardiovascular infection. Dilated cardiomyopathy, Heart failure, Arrhythmia, Pulmonary stenosis, Mitral regurgitation, Coronary artery disease, Myocardial infraction, Mitral valve prolapse, Hypertrophic cardiomyopathy are the sorts of coronary illness. The several machine learning techniques are analyzed to spot heart disease. This paper gives relative investigation of coronary illness expectation utilizing machine learning.
AB - Heart is one of the significant segments in the human body since it powers blood to the all the pieces of the body. Blood courses through the vein. Cardiovascular sickness is corresponded with the blockage of vein. The sign of heart sickness depends whereupon condition is impacting an individual. The term coronary illness is ordinarily utilized instead of cardiovascular infection. Dilated cardiomyopathy, Heart failure, Arrhythmia, Pulmonary stenosis, Mitral regurgitation, Coronary artery disease, Myocardial infraction, Mitral valve prolapse, Hypertrophic cardiomyopathy are the sorts of coronary illness. The several machine learning techniques are analyzed to spot heart disease. This paper gives relative investigation of coronary illness expectation utilizing machine learning.
UR - http://www.scopus.com/inward/record.url?scp=85124645239&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124645239&partnerID=8YFLogxK
U2 - 10.1109/ICDABI53623.2021.9655908
DO - 10.1109/ICDABI53623.2021.9655908
M3 - Conference contribution
AN - SCOPUS:85124645239
T3 - 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
SP - 214
EP - 217
BT - 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
Y2 - 25 October 2021 through 26 October 2021
ER -
