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

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

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