Artificial Intelligence in Cardiology: Machine Learning Techniques for Heart Disease

This textbook provides an in-depth exploration of how machine learning algorithms can be effectively applied to detect and classify heart disease. It bridges the gap between healthcare and computational intelligence by presenting theoretical foundations, practical implementations, and real-world applications of machine learning in cardiology. Starting with an overview of cardiovascular diseases and their global impact, the book delves into essential medical features and datasets relevant to heart disease. It then systematically explores various machine learning techniques—including decision trees, support vector machines, neural networks, k-nearest neighbours, ensemble methods, and deep learning—and their roles in predictive modelling. Each chapter includes detailed algorithmic explanations, model evaluation metrics (such as accuracy, precision, recall, F1-score, and ROC-AUC), and case studies using publicly available datasets like the Cleveland Heart Disease dataset. Ethical considerations, data privacy, and challenges in clinical deployment are also discussed. This textbook serves as a valuable resource for students, researchers, data scientists, and healthcare professionals.