Predictive Modeling in Biomedical Data Mining and Analysis

Herausgeber: Agarwal, Basant; Balas, Valentina Emilia; Roy, Sudipta; Mittal, Mamta in Delhi Skill & Entrepreneurship University (under Government of NCT Delhi); Goyal, Lalit Mohan

• Broschiertes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Technology
• Seitenzahl: 344
• Erscheinungstermin: 26. August 2022
• Englisch
• Abmessung: 191mm x 236mm x 25mm
• Gewicht: 722g
• ISBN-13: 9780323998642
• ISBN-10: 032399864X
• Artikelnr.: 63512456

Inhaltsangabe

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