Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients - Couverture souple

H S, Suresh Kumar ; C N, Pushpa

9786209063152: Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients

Couverture souple

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

Editeur : LAP Lambert Academic Publishing, 2026

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This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Na�ve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness.

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2026
Langue: anglais
ISBN 10: 6209063152
ISBN 13: 9786209063152
Reliure: Broch�
Nombre de pages: 196
Coordonn�es du fabricant: non disponible
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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients

H S, Suresh Kumar; C N, Pushpa

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients

H S, Suresh Kumar; C N, Pushpa

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients

H S, Suresh Kumar; C N, Pushpa

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients (Paperback)

Suresh Kumar H.S.

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Paperback. Etat : new. Paperback. This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naive Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients

Suresh Kumar H S

Edit� par Omniscriptum, LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Na�ve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. 196 pp. Englisch. N� de r�f. du vendeur 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients (Paperback)

Suresh Kumar H.S.

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Paperback. Etat : new. Paperback. This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naive Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients (Paperback)

Suresh Kumar H.S.

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Paperback. Etat : new. Paperback. This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naive Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N� de r�f. du vendeur 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients | An AI-Based Clinical Risk Assessment Approach

Suresh Kumar H S (u. a.)

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Taschenbuch. Etat : Neu. Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients | An AI-Based Clinical Risk Assessment Approach | Suresh Kumar H S (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209063152 | Verantwortliche Person f�r die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabr�ck, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. N� de r�f. du vendeur 134642441

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients

Suresh Kumar H S

Edit� par LAP LAMBERT Academic Publishing Feb 2026, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Na�ve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 196 pp. Englisch. N� de r�f. du vendeur 9786209063152

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Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients : An AI-Based Clinical Risk Assessment Approach.DE

Suresh Kumar H S

Edit� par Omniscriptum, LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209063152 ISBN 13 : 9786209063152

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Na�ve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. N� de r�f. du vendeur 9786209063152

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