Predictive Modeling in Biomedical Data Mining and Analysis

Erscheinungsjahr 2022
ISBN: 978-0-323-99864-2
Verlag: Elsevier Science & Technology

Buch, Englisch, 344 Seiten, Format (B × H): 236 mm x 191 mm, Gewicht: 722 g

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.

Autoren/Hrsg.

Agarwal, Basant

Roy, Sudipta

Goyal, Lalit Mohan

Balas, Valentina Emilia

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

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

Über Autor(innen)

Roy, Sudipta
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

Agarwal, Basant
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.

Goyal, Lalit Mohan
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.