Inhaltsangabe
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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Über die Autorin bzw. den Autor
Dr. M. Lakshmi Prasad is an Associate Professor in the Department of Computer Science and Engineering at the Institute of Aeronautical Engineering (IARE), Hyderabad. He currently serves as the Head of CSE Division-I, overseeing academic coordination for multiple undergraduate sections across all four years.
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786208443504
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786208443504
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Taschenbuch. Zustand: 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 Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. Bestandsnummer des Verkäufers 9786208443504
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Taschenbuch. Zustand: Neu. Artificial Intelligence in Cardiology | Machine Learning Techniques for Heart Disease | Lakshmi Mudarakola Prasad (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208443504 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 133335963
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786208443504
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