Machine Learning in Biomedical and Health Informatics

Nyhet

Current Applications and Challenges

Inbunden, Engelska, 2025

Av Sudip Kumar Sahana, Rajendrani Mukherjee, Panchali Datta Choudhury, Prasenjit Chatterjee

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Machine learning is playing an indispensable role in framing clinical decisions and enhancing accuracy. This new book offers 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 using chemotherapy and androgen deprivation therapy for prostate cancer and for tracking diseases such as Parkinson’s Speech, Covid-19, and others. Case studies are included that demonstrate the practical use of ML in healthcare informatics.

Produktinformation

Utgivningsdatum2025-09-22
Mått156 x 234 x 20 mm
Vikt700 g
FormatInbunden
SpråkEngelska
SerieAAP Research Notes on Optimization and Decision Making Theories
Antal sidor266
FörlagApple Academic Press Inc.
ISBN9781774919545

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Sudip Kumar Sahana, PhD, is an Associate Professor of Computer Science and Engineering at the Birla Institute of Technology, Mesra, India. His research and teaching interests include soft computing, computational intelligence, distributed computing, and artificial intelligence. He has authored many articles, research papers, and books and is also an editorial board member and reviewer for several reputed journals. He is also the 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.Rajendrani Mukherjee, PhD, is an Associate Professor of Computer Systems & Information Technology at the Institute of Engineering and Management of the University of Engineering and Management, Kolkata, India. She was formerly affiliated with the Calcutta Institute of Engineering and Management and with multinational corporations such as IBM and Fuzzy Logix. She has published journal and conference research papers and book chapters and served as a conference session chair.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 Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has published over 130 research papers in international journals and peer-reviewed conferences and 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 for his work. He is editor of several book series.

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's Disease Prediction 9. Evaluating the Performance of Tree-Based Classifiers for 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