Deep Learning in Cardiovascular Health : Sustainable Al Approaches for Heart Disease Diagnosis and Treatment (Information Systems Engineering and Management)

Nag, Anindya (EDT)/ Hassan, Md. Mehedi (EDT)/ Bairagi, Anupam Kumar (EDT)

Springer Nature Switzerland AG（2026/02発売）

• 製本 Hardcover:ハードカバー版／ページ数 445 p.
• 言語 ENG
• 商品コード 9783032124692

Full Description

This book showcases the most recent developments in the application of artificial intelligence to cardiology and medical imaging, with an emphasis on precise diagnosis, early prediction, and patient-centered care. In order to overcome clinical data ambiguity and enhance confidence in automated systems, it presents innovative frameworks that combine deep learning, fuzzy graph neural networks, metaheuristic optimization, and explainable AI. This book bridges the gap between state-of-the-art research and practical healthcare applications by covering a wide range of techniques, including CNNs, RNNs, residual networks, federated learning, and multimodal learning. As a research reference and a manual for implementing AI-driven healthcare solutions, it provides useful tools, datasets, and methodologies that foster innovation in precision medicine and medical decision-making. It is designed for researchers, clinicians, and students.

Contents

Introduction to Artificial Intelligence in Heart Disease Diagnostics.- Bridging Healthcare Gaps: Machine Learning Solutions for Cardiovascular Disease in Low Resource Settings.- Enhanced Cardiovascular Disease Prediction Using Machine Learning and Deep Learning Models with Optimized Feature Selection Techniques.- Effect of Metaheuristic Feature Selection Techniques for Cardiovascular Health.- FCVD ResNet An Interpretable Deep Residual Network for Cardiovascular Disease Risk Prediction.