Machine Learning in Cardiology: A Practical R-Based Approach - Softcover

Segar, Matthew W.

Softcover

ISBN 13: 9798992730500

Publisher: Matthew W. Segar, 2025

Synopsis

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.

About this title

From the Back Cover

Machine Learning in Cardiology: A Practical R-Based Approach

This hands-on resource offers a comprehensive, step-by-step approach to modernizing cardiac care with data-driven strategies, guiding readers from foundational R programming to deploying advanced predictive models. Whether you're a clinician eager to incorporate AI into patient care, a data scientist expanding your healthcare expertise, or a student exploring one of the most dynamic areas in medicine, this book supplies the technical know-how and clinical insights needed to excel.

Inside, you'll learn how to:
* Set up an R environment tailored for medical data exploration, visualization, and reproducible research
* Process real-world clinical data—from handling missing values and outliers to integrating EHR, imaging, and genomic information
* Develop and refine cardiac risk prediction models to identify high-risk patients before adverse events occur
* Harness advanced neural networks, such as CNNs and RNNs, to detect arrhythmias, classify ECG waveforms, and uncover subtle time- series paterns
* Identify and reduce sources of bias while ensuring fair, transparent decision-making in clinical contexts
* Meet essential regulatory standards and data privacy requirements, including HIPAA and GDPR, while innovating ethically
* Translate models from proof-of-concept to patient-facing implementation, accounting for validation, calibration, and interpretability

Edition details

• Publisher: Matthew W. Segar
• Publication date: 2025
• Language: English
• ISBN 13: 9798992730500
• Binding: Paperback
• Number of pages: 138