
Predictive Modeling in Biomedical Data Mining and Analysis
- 1st Edition - August 26, 2022
- Imprint: Academic Press
- Editors: Sudipta Roy, Lalit Mohan Goyal, Valentina Emilia Balas, Basant Agarwal, Mamta Mittal
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 8 6 4 - 2
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 4 4 5 - 1
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… Read more

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Request a sales quotePredictive 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.
- Includes predictive modeling algorithms for both Supervised Learning and Unsupervised Learning for medical diagnosis, data summarization and pattern identification
- Offers complete coverage of predictive modeling in biomedical applications, including data visualization, information retrieval, data mining, image pre-processing and segmentation, mathematical models and deep neural networks
- Provides readers with leading-edge coverage of biomedical data processing, including high dimension data, data reduction, clinical decision-making, deep machine learning in large data sets, multimodal, multi-task, and transfer learning, as well as machine learning with Internet of Biomedical Things applications
- Cover
- Title page
- Table of Contents
- Copyright
- Contributors
- About the Editors
- Preface
- 1: Data mining with deep learning in biomedical data
- Abstract
- 1: Introduction
- 2: Role of deep learning techniques in epileptic seizure detection
- 3: Proposed method of seizure detection
- 4: Results and discussion
- 5: Conclusions
- References
- 2: Applications of supervised machine learning techniques with the goal of medical analysis and prediction: A case study of breast cancer
- Abstract
- 1: Introduction
- 2: A brief literature survey
- 3: Dataset and modus operandi
- 4: Data visualization
- 5: Feature selection and dimensionality reduction
- 6: Experimental results and discussions
- 7: Conclusions
- References
- 3: Medical decision support system using data mining
- Abstract
- 1: Introduction
- 2: Medical decision support system: A review
- 3: Ontological representation of MDSS
- 4: Integrated medical decision support system
- 5: Conclusion and future enhancement
- References
- 4: Role of AI techniques in enhancing multi-modality medical image fusion results
- Abstract
- Acknowledgment
- 1: Introduction
- 2: Modalities
- 3: Fusion process
- 4: AI based fusion
- 5: Evaluation
- 6: Experimental results
- 7: Conclusion and future scope
- References
- 5: A comparative performance analysis of backpropagation training optimizers to estimate clinical gait mechanics
- Abstract
- 1: Introduction
- 2: Methods: Related work and dataset
- 3: Backpropagation neural network and training optimizers
- 4: BPNN implementation
- 5: Results and discussions
- 6: Conclusions
- References
- 6: High-performance medicine in cognitive impairment: Brain–computer interfacing for prodromal Alzheimer's disease
- Abstract
- 1: Introduction
- 2: Related works
- 3: Methodology
- 4: Results
- 5: Conclusion
- References
- 7: Brain tumor classifications by gradient and XG boosting machine learning models
- Abstract
- 1: Introduction
- 2: Research background
- 3: Methods
- 4: Results and discussions
- 5: Conclusions
- Conflicts of interest
- References
- 8: Biofeedback method for human–computer interaction to improve elder caring: Eye-gaze tracking
- Abstract
- 1: Introduction
- 2: Anatomy of the human eye
- 3: Overview of eye-gaze tracking
- 4: Eye-gaze tracking for human–computer interaction
- 5: Proposed design
- 6: Results
- 7: Conclusion
- References
- 9: Prediction of blood screening parameters for preliminary analysis using neural networks
- Abstract
- 1: Introduction
- 2: Related work
- 3: Methodology
- 4: Results
- 5: Conclusion
- References
- 10: Classification of hypertension using an improved unsupervised learning technique and image processing
- Abstract
- 1: Introduction
- 2: Related work
- 3: Methodology
- 4: Experimental results
- 5: Conclusion
- References
- 11: Biomedical data visualization and clinical decision-making in rodents using a multi-usage wireless brain stimulator with a novel embedded design
- Abstract
- 1: Introduction
- 2: Architectural design and circuit modeling
- 3: Implementation and experimental verification
- 4: Results and discussions
- 5: Conclusion and future directions
- References
- 12: LSTM neural network-based classification of sensory signals for healthy and unhealthy gait assessment
- Abstract
- 1: Introduction
- 2: Dataset collection
- 3: LSTM neural network model
- 4: Implementation of LSTM neural network
- 5: Results and discussions
- 6: Conclusions
- References
- 13: Data-driven machine learning: A new approach to process and utilize biomedical data
- Abstract
- 1: An introduction to artificial intelligence and machine learning in healthcare
- 2: Challenges and roadblocks to be addressed
- 3: The need to address these issues
- 4: Recommendations and guidelines for the improvement of ML-based algorithms
- 5: Applications in the present scenarios
- 6: Future prospects and conclusion
- References
- 14: Multiobjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis
- Abstract
- 1: Introduction
- 2: Medical applications
- 3: Feature selection
- 4: Literature review
- 5: Metaheuristics and MOO
- 6: Multiobjective optimization problems (MOOPs)
- 7: Role of EA in MOO
- 8: MOEA based on decomposition
- 9: Application of MOEA/D in feature selection for medical diagnosis
- 10: Experimental results
- 11: Conclusion
- References
- 15: Machine learning techniques in healthcare informatics: Showcasing prediction of type 2 diabetes mellitus disease using lifestyle data
- Abstract
- 1: Introduction
- 2: Machine learning in healthcare
- 3: Proposed framework
- 4: Results and discussion
- 5: Conclusion and future scope
- References
- Index
- Edition: 1
- Published: August 26, 2022
- Imprint: Academic Press
- No. of pages: 344
- Language: English
- Paperback ISBN: 9780323998642
- eBook ISBN: 9780323914451
SR
Sudipta Roy
LG
Lalit Mohan Goyal
VB
Valentina Emilia Balas
BA
Basant Agarwal
MM