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
Purchase options
Institutional subscription on ScienceDirect
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
Biomedical engineers, computer scientists, data analysts, researchers, and clinicians in biology, neuroscience, biophysics, biochemistry, and applied mathematics; Research scientists and engineers in the fields of medical and biological sciences
- 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
Dr. Sudipta Roy received his Ph.D. in Computer Science & Engineering from the Department of Computer Science and Engineering, University of Calcutta. He is author of more than forty publications in refereed national / international journals and conferences. Dr. Roy holds a US patent in medical image processing, and filed an Indian patent in smart agricultural systems. Dr. Roy serves as an Associate Editor of IEEE Access, and IEEE and International Journal of Computer Vision and Image Processing (IJCVIP). His fields of research interest are biomedical image analysis, image processing, steganography, artificial intelligence, big data analysis, machine learning and big data technologies. Currently, he is a Research Associate at PRTTL, Washington University in St. Louis, Saint Louis, MO, USA
Affiliations and expertise
Research Associate, PRTTL, Washington University, Saint Louis, Missouri, USALG
Lalit Mohan Goyal
Dr. Lalit Mohan Goyal has completed Ph.D. from Jamia Millia Islamia, New Delhi, in Computer Engineering; M.Tech (Honors) in Information Technology from Guru Gobind Singh Indraprastha University, New Delhi; and B.Tech (Honors) in Computer Engineering from Kurukshetra University, Kurukshetra. He has 17 years of teaching experience in the area of Theory of Computation, Parallel and Random algorithms, Distributed Data Mining & Cloud Computing. He has completed a project sponsored by the Indian Council of Medical Research, Delhi. He has published and communicated more than 40 research papers and attended many workshops, FDPs and Seminars. He has filed nine patents in the area of Artificial Intelligence and Deep Learning. He is the reviewer of many reputed journals, conferences book series. Presently, He is working in Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad.
Affiliations and expertise
Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad.VB
Valentina Emilia Balas
Dr. Valentina Emilia Balas is currently a Full Professor at the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a PhD cum laude in applied electronics and telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers in refereed journals and for international conferences. Her research interests cover intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, modeling, and simulation. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, as well as an expert evaluator for national and international projects and PhD theses. Dr. Balas is the Director of the Intelligent Systems Research Center and the Director of the Department of International Relations, Programs and Projects at the “Aurel Vlaicu” University of Arad. She served as the General Chair for nine editions of the International Workshop on Soft Computing Applications (SOFA) organized in 2005–2020 and held in Romania and Hungary. Dr. Balas participated in many international conferences as organizer, honorary chair, session chair, member in steering, advisory or international program committees, and keynote speaker. Now she is working on a national project funded by the European Union: BioCell-NanoART = Novel Bio-inspired Cellular Nano-Architectures. She is a member of the European Society for Fuzzy Logic and Technology, a member of the Society for Industrial and Applied Mathematics, a senior member of IEEE, a member of the IEEE Fuzzy Systems Technical Committee, the chair of Task Force 14 of the IEEE Emergent Technologies Technical Committee, a member of the IEEE Soft Computing Technical Committee. She is also the recipient of the "Tudor Tanasescu" prize from the Romanian Academy for contributions in the field of soft computing methods (2019).
Affiliations and expertise
Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, RomaniaBA
Basant Agarwal
Dr. Basant Agarwal works as an Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India, which is an Institute of National Importance. He holds a Ph.D. and M.Tech. from the Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India. He has more than 9 years of experience in research and teaching. He has worked as a Postdoc Research Fellow at the Norwegian University of Science and Technology (NTNU), Norway, under the prestigious ERCIM (European Research Consortium for Informatics and Mathematics) fellowship in 2016. He has also worked as a Research Scientist at Temasek Laboratories, National University of Singapore (NUS), Singapore. His research interest include Artificial Intelligence, Cyber physical systems, Text mining, Natural Language Processing, Machine learning, Deep learning, Intelligent Systems, Expert Systems and related areas.
Affiliations and expertise
Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India.MM
Mamta Mittal
Dr. Mamta Mittal works as Head and Associate Professor (Data Analytics and Data Science) in Delhi Skill & Entrepreneurship University (under Government of NCT Delhi), New Delhi. She received a PhD in Computer Science and Engineering from Thapar University, Patiala; MTech (Honors) in Computer Science & Engineering from YMCA, Faridabad; and B. Tech in Computer Science & Engineering from Kurukshetra University, Kurukshetra, in 2001. She has been teaching for the last 18 years with emphasis on Data Mining, Machine Learning, DBMS and Data Structure. Dr. Mittal is a lifetime member of CSI and published more than 80 research papers. She holds five patents, two of which have been granted copyrights, and three more published in the area of Artificial Intelligence, IoT and Deep Learning. Dr. Mittal has edited/authored many books with reputed publishers, and is working on DST approved Project “Development of IoT based hybrid navigation module for mid-sized autonomous vehicles”. Currently, she is guiding PhD scholars in Machine Learning, Computer Vision and Deep Learning areas. Dr. Mittal is Editorial Board member with Inder-Science, Bentham Science, Springer and Elsevier, handled Special issues, has chaired many Conferences.
Affiliations and expertise
Associate Professor (Data Analytics and Data Science) in Delhi Skill & Entrepreneurship University (under Government of NCT Delhi), New DelhiRead Predictive Modeling in Biomedical Data Mining and Analysis on ScienceDirect