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Predictive Modeling in Biomedical Data Mining and Analysis
Presents leading-edge research on the applications of predictive modeling algorithms and techniques to biomedical applications
Sudipta Roy (Edited by), Lalit Mohan Goyal (Edited by), Valentina Emilia Balas (Edited by), Basant Agarwal (Edited by), Mamta Mittal (Edited by)
9780323998642, Elsevier Science
Paperback / softback, published 26 August 2022
344 pages, 80 illustrations (30 in full color)
23.5 x 19 x 2.2 cm, 0.7 kg
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. Data mining with deep learning in biomedical data 2. Applications of supervised machine learning techniques with the goal of medical analysis and prediction: a case study of breast cancer 3. Medical decision support system using data mining 4. Role of AI techniques in enhancing multi-modality medical image fusion results 5. A comparative performance analysis of backpropagation training optimizers to estimate clinical gait mechanics 6. High-performance medicine in cognitive impairment: Brain-computer interfacing for prodromal Alzheimer’s disease 7. Machine learning in healthcare: Brain tumor classifications by gradient and XG boosting models 8. Biofeedback method for human-computer interaction to improve elder caring: Eye gaze tracking 9. Blood screening parameters prediction for preliminary analysis using neural networks 10. Classification of hypertension using the improved unsupervised learning technique and image processing 11. Biomedical data visualization and clinical decision-making in rodents using a multi-usage wireless brain stimulator using novel embedded design 12. LSTM neural network-based classification of sensory signals for healthy and unhealthy gait assessment 13. Addressing challenges and roadblocks in iomedical data using data-driven machine learning 14. Multibjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis 15. Machine learning techniques in healthcare informatics: Showcasing prediction of type 2 diabetes mellitus disease using lifestyle data
Subject Areas: Biomedical engineering [MQW]
