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

Suresh Kumar H S

ISBN 10: 6209063152 / ISBN 13: 9786209063152

Edité par Omniscriptum, LAP Lambert Academic Publishing, 2026

A propos de cet article

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.

N° de réf. du vendeur 9786209063152

Détails bibliographiques

• Titre: Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients : An AI-Based Clinical Risk Assessment Approach.DE
• Éditeur: Omniscriptum, LAP Lambert Academic Publishing
• Date de parution: 2026
• Langue: anglais
• ISBN à 10 chiffres: 6209063152
• ISBN à 13 chiffres: 9786209063152
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 220 L x 150 H x 33 P (en millimètres) 220 millimètres de largeur par 150 millimètres de hauteur par 33 millimètres de profondeur

A propos de ce titre

Synopsis

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.