Isbn: 9786208443504 - artificial intelligence in cardiology: machine learning techniques for heart disease (10 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: 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.…

Taschenbuch. Condizione: 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.…

Taschenbuch. Condizione: 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.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: 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.…