Machine Learning for Biomedical Engineers

Vijay Jeyakumar, Venkateswaran N, Dinesh Bhatia, N. Venkateswaran

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781041067948
• Edition: 1
• eBook ISBN:
  9781040649039
• Topic: Technology & Engineering
• Subtopic: Biomedical Science
• Index: Technology & Engineering

This book combines machine learning (ML) and biomedical engineering to address practical issues in healthcare and biomedical research concentrating on real-world applications including bioinformatics, customised medicine, medical imaging analysis, disease detection, and health monitoring. It contains case studies and examples that show how various ML algorithms are used on biomedical data sets. The ethical issues and difficulties unique to using ML in biomedical settings, such as data privacy, algorithm bias, and regulatory compliance are also covered.

• Provides a broad introduction to ML in biomedicine and biomedical engineering
• Discusses ethical considerations and explainability pertinent to ML in bioengineering
• Explores step-by-step tutorials, coding examples, and real-world case studies
• Reviews feature selection, training and evaluating models, preprocessing data, and validation techniques tailored to biomedical data
• Includes MATLAB and Python coding programs

This book is aimed at graduate students and researchers in bioengineering and ML

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Foreword
• Editor Biographies
• Contributors
• 1 Introduction to Machine Learning
• 2 Overview of Medical Data Modalities and Their Representation
• 3 Methods for Biomedical Data Pre-processing in Machine Learning
• 4 Feature Engineering of Biomedical Data
• 5 Machine Learning for Medical Data Classification
• 6 Deep Learning Models for Medical Data Analysis
• 7 Deep Learning and Machine Learning Techniques for Early Prediction of Alzheimer’s Disease
• 8 Electrocardiogram-Based Cardiac Disease Classification Using a Hybrid Fuzzy Machine Learning Framework
• 9 Vision Language Model-Based Health Data Retrieval and Trend Analysis System for Chronic Diseases in Balochistan
• 10 High-Performance Computing in Healthcare
• 11 Ethical and Legal Considerations for Machine Learning and Deep Learning in Biomedical Engineering
• 12 Machine Learning-Driven Dyslexia Detection Based on Eye-Tracking Data
• 13 Acoustic Respiratory Analysis for the Screening of Chronic Obstructive Pulmonary Disease using Machine Learning Techniques
• 14 Comprehensive Feature Insights into Gait Dynamics in Neurodegenerative Diseases: From Spatio-Temporal to Spatio-Spectral Measures
• 15 AI-Driven Muscle Coordination Prediction System for Upper Limb Movement Using EMG Signals
• 16 Real-Time Intelligent Patient Monitoring: A Federated Learning and TinyML-Based Approach
• 17 AI-Driven Face Recognition and Mask Detection for Secure Attendance Management in the Post-Pandemic Era
• 18 Automated Machine Learning for Genetic Disease Prediction Using Chromosome Image Classification
• Open Data Repository Link for Various Domain Databases
• Index