Machine Learning in Cardiology

Machine Learning in Cardiology: A Practical R-Based Approach demystifies how artificial intelligence can revolutionize modern heart care. Written by cardiologist and data scientist Dr. Matthew Segar, this hands-on guide takes you step by step through essential R-based workflows-from data wrangling and visualization to advanced modeling techniques and real-world clinical applications.

You'll learn how to harness supervised and unsupervised learning, master feature engineering for complex cardiac data, and build powerful predictive tools for risk stratification. Dive into specialized topics like ECG signal analysis, survival modeling, and genomic data integration, then see how to implement fairness and bias mitigation strategies to ensure equitable patient outcomes. With clear, annotated R code examples and in-depth discussions about ethics, regulatory landscapes, and reproducible research, this book empowers you to develop robust, trustworthy machine learning systems.

Whether you're a cardiologist, researcher, or data scientist, Machine Learning in Cardiology provides the technical know-how and clinical insights to elevate your practice-and ultimately improve patient care.