Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
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Published by LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Heart disease is one of the foremost critical human diseases within the world and affects human life very badly. In heart conditions, the guts are unable to push the specified amount of blood to other parts of the body. Accurate and on-time diagnosis of heart condition is vital for coronary failure prevention and treatment. The diagnosis of heart condition through traditional medical records has been considered as not reliable in many aspects. To classify healthy people and other people with heart conditions, noninvasive-based methods like machine learning are reliable and efficient. Within the proposed study, we developed a machine-learning-based diagnosis system for heart condition prediction by using a heart condition dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and 7 classifiers performance evaluation metrics like classification accuracy, specificity, sensitivity, Matthews' coefficient of correlation, and execution time. 72 pp. Englisch.
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
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Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Heart disease is one of the foremost critical human diseases within the world and affects human life very badly. In heart conditions, the guts are unable to push the specified amount of blood to other parts of the body. Accurate and on-time diagnosis of heart condition is vital for coronary failure prevention and treatment. The diagnosis of heart condition through traditional medical records has been considered as not reliable in many aspects. To classify healthy people and other people with heart conditions, noninvasive-based methods like machine learning are reliable and efficient. Within the proposed study, we developed a machine-learning-based diagnosis system for heart condition prediction by using a heart condition dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and 7 classifiers performance evaluation metrics like classification accuracy, specificity, sensitivity, Matthews' coefficient of correlation, and execution time.
Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
Seller: Biblios, Frankfurt am main, HESSE, Germany
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Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Patel AdityaProf. Aditya Patel works as an Assistant Professor in CSE Dept. at LNCT Bhopal. Previously he worked as Web Designer & Developer in Ignatiuz Software Private Lmtd.Heart disease is one of the foremost critical human di.
Published by LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10: 6203926221 ISBN 13: 9786203926224
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Heart disease is one of the foremost critical human diseases within the world and affects human life very badly. In heart conditions, the guts are unable to push the specified amount of blood to other parts of the body. Accurate and on-time diagnosis of heart condition is vital for coronary failure prevention and treatment. The diagnosis of heart condition through traditional medical records has been considered as not reliable in many aspects. To classify healthy people and other people with heart conditions, noninvasive-based methods like machine learning are reliable and efficient. Within the proposed study, we developed a machine-learning-based diagnosis system for heart condition prediction by using a heart condition dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and 7 classifiers performance evaluation metrics like classification accuracy, specificity, sensitivity, Matthews¿ coefficient of correlation, and execution time.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch.