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portada Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients
Type
Physical Book
Publisher
Author
Language
English
Pages
196
Format
Paperback
Dimensions
22.9x15.2x1.1 cm
ISBN13
9786209063152

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

H. S., Suresh Kumar; C. N., Pushpa (Author) · LAP Lambert Academic Publishing · Paperback

Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients - H. S., Suresh Kumar; C. N., Pushpa

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

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.

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