Predictive Modeling in Biomedical Data Mining and Analysis presents major technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis. As the application of machine learning is spreading to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this is an ideal reference.
Machine Learning techniques are used as predictive models for many types of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xi 7. About the Editors - p. xv 8. Preface - p. xix 9. 1. Data mining with deep learning in biomedical data - p. 1 10. 2. Applications of supervised machine learning techniques with the goal of medical analysis and prediction: A case study of breast cancer - p. 25 11. 3. Medical decision support system using data mining - p. 49 12. 4. Role of AI techniques in enhancing multi-modality medical image fusion results - p. 65 13. 5. A comparative performance analysis of backpropagation training optimizers to estimate clinical gait mechanics - p. 83 14. 6. High-performance medicine in cognitive impairment: Brain–computer interfacing for prodromal Alzheimer's disease - p. 105 15. 7. Brain tumor classifications by gradient and XG boosting machine learning models - p. 123 16. 8. Biofeedback method for human–computer interaction to improve elder caring: Eye-gaze tracking - p. 137 17. 9. Prediction of blood screening parameters for preliminary analysis using neural networks - p. 157 18. 10. Classification of hypertension using an improved unsupervised learning technique and image processing - p. 171 19. 11. Biomedical data visualization and clinical decision-making in rodents using a multi-usage wireless brain stimulator with a novel embedded design - p. 187 20. 12. LSTM neural network-based classification of sensory signals for healthy and unhealthy gait assessment - p. 207 21. 13. Data-driven machine learning: A new approach to process and utilize biomedical data - p. 225 22. 14. Multiobjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis - p. 287 23. 15. Machine learning techniques in healthcare informatics: Showcasing prediction of type 2 diabetes mellitus disease using lifestyle data - p. 295 24. Index - p. 313
Predictive Modeling in Biomedical Data Mining and Analysis presents major technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis. As the application of machine learning is spreading to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this is an ideal reference.
Machine Learning techniques are used as predictive models for many types of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xi 7. About the Editors - p. xv 8. Preface - p. xix 9. 1. Data mining with deep learning in biomedical data - p. 1 10. 2. Applications of supervised machine learning techniques with the goal of medical analysis and prediction: A case study of breast cancer - p. 25 11. 3. Medical decision support system using data mining - p. 49 12. 4. Role of AI techniques in enhancing multi-modality medical image fusion results - p. 65 13. 5. A comparative performance analysis of backpropagation training optimizers to estimate clinical gait mechanics - p. 83 14. 6. High-performance medicine in cognitive impairment: Brain–computer interfacing for prodromal Alzheimer's disease - p. 105 15. 7. Brain tumor classifications by gradient and XG boosting machine learning models - p. 123 16. 8. Biofeedback method for human–computer interaction to improve elder caring: Eye-gaze tracking - p. 137 17. 9. Prediction of blood screening parameters for preliminary analysis using neural networks - p. 157 18. 10. Classification of hypertension using an improved unsupervised learning technique and image processing - p. 171 19. 11. Biomedical data visualization and clinical decision-making in rodents using a multi-usage wireless brain stimulator with a novel embedded design - p. 187 20. 12. LSTM neural network-based classification of sensory signals for healthy and unhealthy gait assessment - p. 207 21. 13. Data-driven machine learning: A new approach to process and utilize biomedical data - p. 225 22. 14. Multiobjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis - p. 287 23. 15. Machine learning techniques in healthcare informatics: Showcasing prediction of type 2 diabetes mellitus disease using lifestyle data - p. 295 24. Index - p. 313
