Machine Learning for Biomedical Engineers by Jeyakumar, Vijay; N, Venkateswaran; Bhatia, Dinesh;

Short description:

This book combines machine learning and biomedical engineering to address practical issues in healthcare and biomedical research concentrating on real-world applications including bioinformatics, customized medicine, medical imaging analysis, disease detection, and health monitoring.

Long description:

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

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

This book is aimed at graduate students and researchers in bioengineering and machine learning.

Table of Contents:

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 8. A Cardiac Disease Classification of ECG Signal using Hybrid Fuzzy Machine Learning Algorithm 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 Techniques for predicting genetic disease by classifying chromosome image