9786209063152 - Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients: An AI-Based Clinical Risk Assessment Approach de H S, Suresh Kumar; C N, Pushpa (12 resultados)

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

Condición: New.

Taschenbuch. Condición: 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 für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Paperback. Condición: 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.

Taschenbuch. Condición: 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 Naïve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. 196 pp. Englisch.

Paperback. Condición: 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.

Paperback. Condición: 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.

Taschenbuch. Condición: 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 Naïve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch.

Condición: New. Print on Demand.

Taschenbuch. Condición: 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 Naïve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.