Machine Learning in Biomedical and Health Informatics

Current Applications and Challenges
1. Auflage 2025
ISBN: 978-1-77491-954-5
Verlag: Apple Academic Press Inc.

Buch, Englisch, 290 Seiten, Format (B × H): 156 mm x 234 mm

With rapid development in healthcare, the domain of health analytics via IDA (intelligent data analysis) and health informatics is continuously growing. Machine learning is playing an indispensable role in framing clinical decisions and enhancing its accuracy. This new book, Machine Learning in Biomedical and Health Informatics: Current Applications and Challenges, is a comprehensive take on the field of biomedical and health informatics discussing topics that include predictive health analytics, pandemic management, AI ethics, application and integration of Internet of Things and Machine Learning for effective healthcare, and more.

The book covers a range of bioinformatics tools and methods and their relation to drug designing and drug screening using ML. Several chapters cover clustering techniques and other methods for analyzing human heart-related disorders. The authors also explore the use of ML in creating adaptive therapies, for example, for using chemotherapy and androgen deprivation therapy for prostate cancer. Use of ML for tracking diseases such as Parkinson’s speech, Covid-19, and others and survival rates of deadly diseases like cancer are also discussed. The book also demonstrates a framework for big data classification using singular value decomposition, which is applied to various medical datasets. Also discussed is medical images analysis and using different AI techniques for solving problem areas in medical images by considering X-rays, MRI, PET. Various case studies are also included that demonstrate the practical use of ML in healthcare informatics. The book also reviews a few major applications of ML in bioinformatics like identification of patterns and relationships in genomic data.

This book will prove beneficial for researchers and technocrats as well as for students, providing an in-depth and illustrated work on the use of machine learning in biomedical and health informatics.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Choudhury, Panchali Datta

Sahana, Sudip Kumar

Chatterjee, Prasenjit

Mukherjee, Rajendrani

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Role of Machine Learning in High Throughput Screening of Drug Molecules 2. Solving a Capacitated Vehicle Routing Problem with Time Windows Using Dijkstra’s Algorithm: A Case Study on Covid Vaccine Distribution 3. Heart Disease Prediction: A Clustering-Based Clinical Decision Support Approach 4. Application of Fuzzy Tools to Avoid Reoccurrence of Prostate Cancer Post-Treatment 5. Machine Learning: A Quantum Leap in Data Mining Modalities for Healthcare Upliftment 6. Impact of Matrix-Factorization-Based Dimensionality Reduction in the Prediction of Diseases 7. Applications of Bioinformatics and Machine Learning Algorithms in Survival Analysis of Cancer Patients 8. Speech Signal Analysis Using Gammatone-Frequency Cepstral Coefficient for Parkinson Disease Prediction 9. Evaluating the Performance of Tree-Based Classifiers for the Task of Predicting Marginal and Acute Cardiovascular Diseases: A Comprehensive Review 10. Human Health Data Analysis Using Machine Learning 11. COVIDIncResNet: An Efficient Approach for CNN-Based Covid Classification Model Using ECG Images 12. The Role of Artificial Intelligence in Medical Image Analysis for Disease Diagnosis 13. Application of Machine Learning in Bioinformatics: Capture and Interpret Biological Data

Über Autor(innen)

Sudip Kumar Sahana, PhD, is currently working as Associate Professor in the Department of Computer Science and Engineering at the Birla Institute of Technology, Mesra, India. His major field of study is in computer science. His research and teaching interests include soft computing, computational intelligence, distributed computing, and artificial intelligence. He has authored numerous articles, research papers, and books in the field of computer science and has been assigned as an editorial team member and reviewer for several reputed journals. He is also inventor of five patents in the field of artificial intelligence. He has carried out numerous R&D-sponsored projects of around 1.22 million USD. He is a lifetime member of the Indian Society for Technical Education (ISTE) and fellow of the Institution of Electronics and Telecommunication Engineers (IETE), India. He has successfully supervised four PhD scholars and currently three are ongoing.

Rajendrani Mukherjee, PhD, has been working as an Associate Professor at the University of Engineering and Management, Kolkata, India, since 2020. Prior to that, she has worked as Assistant Professor at the Calcutta Institute of Engineering and Management. She has also worked in the software industry for five years with prominent multinational corporations such as IBM and Fuzzy Logix. She became a IBM Certified Database Associate in 2007. She has published many journal and conference research papers and book chapters. Her research interest mainly lies in machine learning, data analytics, process modeling, and the automation software engineering domain. She has served as session chair (CIPR 2021, IEEE IEMECON 2022) and technical committee member (INDICON 2021, IEEE CCWC 2022, COMSYS 2023) of many conferences. She is also reviewer for several SCI-indexed journals. She has served as mentor of the finalist team at national level Smart India Hackathon in 2019. In 2021, she became AICTE-certified mentor from NITTTR Chennai. She was a member of the Phi Kappa Phi honor society (2009) for highest achievement in her graduating class. She is an active member of IEEE.

Panchali Datta Choudhury, PhD, is an Assistant Professor at the University of Engineering and Management, Kolkata, India, in the Department of Computer Science and Technology. She completed her PhD in Computer Science and Engineering at the National Institute of Technology, Durgapur, India. Her research interest includes optical networking and protection management in optical networks. She is a member of the Optical Society of America and IEEE.

Prasenjit Chatterjee, PhD, is currently Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has over 6400 citation and has published over 130 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 15 books on intelligent decision-making, supply chain management, optimization techniques, risk, and sustainability modeling. He has received numerous awards, including best track paper awards, outstanding reviewer awards, best paper awards, outstanding researcher awards, and university gold medals. He has been a guest editor of several special issues of various indexed journals. He has authored and edited several books on decision analysis, disruptive technologies, intelligent computing, supply chains, and sustainability modeling. He is the Lead Series Editor of the book series Disruptive Technologies and Digital Transformations for Society 5.0. He is the Founder and Lead Series Editor of the book series Concise Introductions to AI and Data Science; AAP Research Notes on Optimization and Decision-Making Theories; and Frontiers of Mechanical and Industrial Engineering; Smart and Intelligent Computing in Engineering. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).