Artificial Intelligence in Cardiology: Machine Learning Techniques for Heart Disease - Tapa blanda

Mudarakola Prasad, Lakshmi ; Ch, Srinivasulu ; Padmaja, M

9786208443504: Artificial Intelligence in Cardiology: Machine Learning Techniques for Heart Disease

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ISBN 10: 6208443504 ISBN 13: 9786208443504

Editorial: LAP LAMBERT Academic Publishing, 2025

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This textbook provides an in-depth exploration of how machine learning algorithms can be effectively applied to detect and classify heart disease. It bridges the gap between healthcare and computational intelligence by presenting theoretical foundations, practical implementations, and real-world applications of machine learning in cardiology. Starting with an overview of cardiovascular diseases and their global impact, the book delves into essential medical features and datasets relevant to heart disease. It then systematically explores various machine learning techniques—including decision trees, support vector machines, neural networks, k-nearest neighbours, ensemble methods, and deep learning—and their roles in predictive modelling. Each chapter includes detailed algorithmic explanations, model evaluation metrics (such as accuracy, precision, recall, F1-score, and ROC-AUC), and case studies using publicly available datasets like the Cleveland Heart Disease dataset. Ethical considerations, data privacy, and challenges in clinical deployment are also discussed. This textbook serves as a valuable resource for students, researchers, data scientists, and healthcare professionals.

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Editorial: LAP LAMBERT Academic Publishing
A�o de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 6208443504
ISBN 13: 9786208443504
Encuadernaci�n: Tapa blanda
N�mero de p�ginas: 100
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Artificial Intelligence in Cardiology

Mudarakola Prasad, Lakshmi; Ch, Srinivasulu; Padmaja, M

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208443504 ISBN 13: 9786208443504

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Artificial Intelligence in Cardiology

Mudarakola Prasad, Lakshmi; Ch, Srinivasulu; Padmaja, M

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208443504 ISBN 13: 9786208443504

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Artificial Intelligence in Cardiology: Machine Learning Techniques for Heart Disease

Mudarakola Prasad, Lakshmi; Ch, Srinivasulu; Padmaja, M

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208443504 ISBN 13: 9786208443504

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Artificial Intelligence in Cardiology

Lakshmi Mudarakola Prasad

Publicado por Omniscriptum, LAP Lambert Academic Publishing, 2025

ISBN 10: 6208443504 ISBN 13: 9786208443504

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Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook provides an in-depth exploration of how machine learning algorithms can be effectively applied to detect and classify heart disease. It bridges the gap between healthcare and computational intelligence by presenting theoretical foundations, practical implementations, and real-world applications of machine learning in cardiology. Starting with an overview of cardiovascular diseases and their global impact, the book delves into essential medical features and datasets relevant to heart disease. It then systematically explores various machine learning techniques-including decision trees, support vector machines, neural networks, k-nearest neighbours, ensemble methods, and deep learning-and their roles in predictive modelling. Each chapter includes detailed algorithmic explanations, model evaluation metrics (such as accuracy, precision, recall, F1-score, and ROC-AUC), and case studies using publicly available datasets like the Cleveland Heart Disease dataset. Ethical considerations, data privacy, and challenges in clinical deployment are also discussed. This textbook serves as a valuable resource for students, researchers, data scientists, and healthcare professionals. 100 pp. Englisch. N� de ref. del art�culo: 9786208443504

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Artificial Intelligence in Cardiology : Machine Learning Techniques for Heart Disease

Srinivasulu Ch

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208443504 ISBN 13: 9786208443504

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Taschenbuch. Condici�n: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This textbook provides an in-depth exploration of how machine learning algorithms can be effectively applied to detect and classify heart disease. It bridges the gap between healthcare and computational intelligence by presenting theoretical foundations, practical implementations, and real-world applications of machine learning in cardiology. Starting with an overview of cardiovascular diseases and their global impact, the book delves into essential medical features and datasets relevant to heart disease. It then systematically explores various machine learning techniques-including decision trees, support vector machines, neural networks, k-nearest neighbours, ensemble methods, and deep learning-and their roles in predictive modelling. Each chapter includes detailed algorithmic explanations, model evaluation metrics (such as accuracy, precision, recall, F1-score, and ROC-AUC), and case studies using publicly available datasets like the Cleveland Heart Disease dataset. Ethical considerations, data privacy, and challenges in clinical deployment are also discussed. This textbook serves as a valuable resource for students, researchers, data scientists, and healthcare professionals. N� de ref. del art�culo: 9786208443504

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Artificial Intelligence in Cardiology

Lakshmi Mudarakola Prasad

Publicado por LAP LAMBERT Academic Publishing Apr 2025, 2025

ISBN 10: 6208443504 ISBN 13: 9786208443504

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Taschenbuch. Condici�n: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This textbook provides an in-depth exploration of how machine learning algorithms can be effectively applied to detect and classify heart disease. It bridges the gap between healthcare and computational intelligence by presenting theoretical foundations, practical implementations, and real-world applications of machine learning in cardiology. Starting with an overview of cardiovascular diseases and their global impact, the book delves into essential medical features and datasets relevant to heart disease. It then systematically explores various machine learning techniques-including decision trees, support vector machines, neural networks, k-nearest neighbours, ensemble methods, and deep learning-and their roles in predictive modelling. Each chapter includes detailed algorithmic explanations, model evaluation metrics (such as accuracy, precision, recall, F1-score, and ROC-AUC), and case studies using publicly available datasets like the Cleveland Heart Disease dataset. Ethical considerations, data privacy, and challenges in clinical deployment are also discussed. This textbook serves as a valuable resource for students, researchers, data scientists, and healthcare professionals.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 100 pp. Englisch. N� de ref. del art�culo: 9786208443504

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