Machine Learning in Cardiology:
A Practical R-Based Approach
A practical primer for clinicians using R to implement machine learning in cardiovascular care
About the Book
"Machine Learning in Cardiology: A Practical R-Based Approach" is a comprehensive primer for clinicians looking to implement AI in cardiovascular practice. 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. 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.
Table of Contents
- Introduction to Machine Learning in Cardiovascular Medicine
- Setting Up Your Environment for R Fundamentals
- Machhine Learning Foundations
- Data Wrangling for Cardiovascular Datasets
- Introduction to Risk Prediction
- ECG Signal Processing with Machine Learning
- Survival Analysis for Cardiovascular Outcomes
- Integrating Genomic Data in Cardiac Analysis
- Ethics, Fairness, and Bias in Cardiovascular AI
- Navigating the Regulatory Landscape
- Public Cardiology Datasets
Key Topics
- Essential R-based workflows for cardiovascular data science
- Supervised and unsupervised learning techniques for clinical applications
- Feature engineering strategies for complex cardiac datasets
- ECG signal analysis and interpretation using machine learning
- Survival modeling for cardiovascular outcomes
- Genomic data integration in cardiac care
- Fairness and bias mitigation strategies for equitable patient outcomes
- Implementation of predictive tools for risk stratification
- Ethics, regulatory considerations, and reproducible research principles
Who This Book Is For
Whether you're a cardiologist looking to incorporate AI into your practice, a researcher exploring new analytical methods, or a data scientist specializing in healthcare applications, "Machine Learning in Cardiology" provides the technical know-how and clinical insights to elevate your work—and ultimately improve patient care.
Advance Your Knowledge in Cardiovascular AI
Join the growing community of medical professionals using machine learning to transform cardiovascular care.
Get Your Copy TodayWhat Experts Are Saying
"As a resident, it helped me understand how to use R for cardiovascular medicine... This book helped explain complex concepts in an accessible way. The R code examples were incredibly helpful."
"This book has a nice balance between technical depth and clinical relevance. The case studies and implementations are particularly helpful for those wanting a primer in this space."
About the Author
Dr. Matthew Segar is a cardiac electrophysiology fellow at the Texas Heart Institute with extensive experience in both clinical cardiology and data science. He holds degrees in Computer Science from Bucknell University and a Masters of Science in Bioinformatics from Indiana University, along with his medical degree from Indiana University School of Medicine.
As the developer of the validated WATCH-DM Risk Score and other clinical prediction tools, Dr. Segar has established himself as a leader in applying machine learning techniques to cardiovascular medicine. His research focuses on improving risk prediction and tailoring medical therapies through artificial intelligence.