menu
{ "item_title" : "Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients", "item_author" : [" Suresh Kumar H. S.", "Pushpa C. N. "], "item_description" : "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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/6/20/906/315/6209063152_b.jpg", "price_data" : { "retail_price" : "92.68", "online_price" : "92.68", "our_price" : "92.68", "club_price" : "92.68", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data-Driven Models for COVID-19 Severity Analysis in Comorbid Patients|Suresh Kumar H. S.

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

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9786209063152
  • ISBN-10: 6209063152
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: February 2026
  • Dimensions: 9 x 6 x 0.45 inches
  • Shipping Weight: 0.59 pounds
  • Page Count: 196

Related Categories

You May Also Like...

    1

BAM Customer Reviews