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.

Highlights the use of different deep learning approaches in evaluating ECG signals, imaging data, patient health records Focuses on sustainable Al approaches for heart disease diagnosis and treatment Delves into different deep learning approaches, such as convolutional neural networks, recurrent neural networks

Inhalt

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.