Applied Machine Learning in Healthcare
Case-Based Approach
Published on 29. December 2025
372 pages
978-1-040-50858-9 (ISBN)
Description
This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and personalized treatment planning. The role of machine learning in enhancing clinical decision support systems (CDSS) is examined in detail, with a focus on its impact on informed decision-making, predictive modelling, and real-time patient monitoring.
* Features real-world case studies and applications that demonstrate the practical use of machine learning in healthcare, including radiology, predictive analytics, personalised medicine, and resource optimisation.
* Covers essential stages of data preprocessing and feature engineering for healthcare datasets, addressing challenges such as data cleaning, normalisation, dimensionality reduction, and feature selection.
* Provides an in-depth overview of CDSS and the integration of machine learning algorithms to improve diagnostic accuracy and clinical workflow efficiency.
* Explores machine learning-driven real-time monitoring and alert systems, underscoring their utility in promptly identifying and responding to critical medical events.
* Discusses advances in medical image analysis, including segmentation, classification, and computer-aided diagnosis techniques.
This comprehensive volume serves as a valuable resource for researchers, clinicians, healthcare professionals, data scientists, and students seeking to understand and apply machine learning for improved healthcare outcomes.
* Features real-world case studies and applications that demonstrate the practical use of machine learning in healthcare, including radiology, predictive analytics, personalised medicine, and resource optimisation.
* Covers essential stages of data preprocessing and feature engineering for healthcare datasets, addressing challenges such as data cleaning, normalisation, dimensionality reduction, and feature selection.
* Provides an in-depth overview of CDSS and the integration of machine learning algorithms to improve diagnostic accuracy and clinical workflow efficiency.
* Explores machine learning-driven real-time monitoring and alert systems, underscoring their utility in promptly identifying and responding to critical medical events.
* Discusses advances in medical image analysis, including segmentation, classification, and computer-aided diagnosis techniques.
This comprehensive volume serves as a valuable resource for researchers, clinicians, healthcare professionals, data scientists, and students seeking to understand and apply machine learning for improved healthcare outcomes.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
23 Tables, black and white; 96 Line drawings, black and white; 9 Halftones, black and white; 105 Illustrations, black and white
File size
25,63 MB
ISBN-13
978-1-040-50858-9 (9781040508589)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Dattatray G. Takale | Parikshit N Mahalle | Sachin S. Bere
Applied Machine Learning in Healthcare
Case-Based Approach
Book
approx. 12/2025
1st Edition
Chapman & Hall/CRC
€212.20
Not yet published
Persons
Dattatray G. Takale is an assistant professor in the Department of Computer Engineering at Vishwakarma Institute of Information Technology, Pune, India. Dr. Takale earned his Ph.D. in computer science and engineering. He has over 12 years of teaching and research experience. His research interests include machine learning, data science, wireless sensor networks, natural language processing, data warehousing, mining, computer networks, and network security. He has more than 9 years of teaching experience and 3 years of industry experience. He has 80 patents, 100+ research publications, and has authored/edited 7+ books with reputed local and international publishers.
Parikshit N. Mahalle is a Senior Member of IEEE and currently serves as Professor and Dean of Academics at Vishwakarma Institute of Technology, Pune, India. He previously held roles as Head of the Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology and as Professor and Head of Computer Engineering at Sinhgad Institutes. He earned his Ph.D. from Aalborg University, Denmark, and completed post-doctoral research at CMI, Copenhagen. With over 25 years of academic and research experience, Dr. Mahalle has guided 8 Ph.D. scholars (7 awarded) and mentored 3 postdoctoral researchers. He has authored or edited 72 books with international publishers. His scholarly output includes more than 430 publications, over 4000 Google Scholar citations (h-index 28), and 2200+ Scopus citations (h-index 21). Dr. Mahalle is the Editor-in-Chief of the Research Journal of Computer Systems and Engineering (RJCSE) and serves as Associate Editor and reviewer for several reputed journals and conferences. His research interests include machine learning, IoT, data science, identity management, and cybersecurity. He has delivered more than 400 invited talks at national and international forums and received prestigious honors including the IEEE ICTBIG 2024 Distinguished Research Guide Award, State Level Meritorious Teacher Award, and International Distinguished Researcher of the Year (S4DS, 2023). His textbook on Design and Analysis of Algorithms is adopted by IIITs and NITs, and his CRC Press book on pandemic data analysis has earned two international awards. In 2024, his edited volume Data Science: Techniques and Intelligent Applications received the Choice Outstanding Academic Titles Award. He is also an ISO 27001:2022 Certified Lead Auditor and has served as guest faculty at institutions including National Taipei University, Taiwan, and UMA, Peru.
Sachin S. Bere is an associate professor in the Dattakala Group of Institutions Faculty of Engineering Bhigwan. He completed his Ph.D. in Computer Science and Engineering from the SJJT University, Rajasthan. He also completed his MTech (CSE) with First Class & Distinction from a JNTU-Hyderabad-affiliated college. He has 18 years of teaching experience and 7 years of research experience. He published almost 30 research articles in reputed journals and conferences. His areas of interest are machine learning, artificial intelligence, deep learning techniques, and programming languages.
Piyush P. Gawali is an Assistant Professor in the Department of Computer Engineering at Vishwakarma Institute of Information Technology, Pune, India. Mr. Piyush Prabhat Gawali earned his M.E. in computer science and engineering and is pursuing a Ph.D. from Savitribai Phule Pune University. He has more than 16 years of teaching experience. His research interests include quantum computing, cybersecurity, medical cyber-physical systems, machine learning, and network security. He has more than 13 years of teaching experience and two years and six months of industry experience. He has 8 patents, 14+ research publications, and has authored/edited 2+ books with local and international publishers.
Parikshit N. Mahalle is a Senior Member of IEEE and currently serves as Professor and Dean of Academics at Vishwakarma Institute of Technology, Pune, India. He previously held roles as Head of the Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology and as Professor and Head of Computer Engineering at Sinhgad Institutes. He earned his Ph.D. from Aalborg University, Denmark, and completed post-doctoral research at CMI, Copenhagen. With over 25 years of academic and research experience, Dr. Mahalle has guided 8 Ph.D. scholars (7 awarded) and mentored 3 postdoctoral researchers. He has authored or edited 72 books with international publishers. His scholarly output includes more than 430 publications, over 4000 Google Scholar citations (h-index 28), and 2200+ Scopus citations (h-index 21). Dr. Mahalle is the Editor-in-Chief of the Research Journal of Computer Systems and Engineering (RJCSE) and serves as Associate Editor and reviewer for several reputed journals and conferences. His research interests include machine learning, IoT, data science, identity management, and cybersecurity. He has delivered more than 400 invited talks at national and international forums and received prestigious honors including the IEEE ICTBIG 2024 Distinguished Research Guide Award, State Level Meritorious Teacher Award, and International Distinguished Researcher of the Year (S4DS, 2023). His textbook on Design and Analysis of Algorithms is adopted by IIITs and NITs, and his CRC Press book on pandemic data analysis has earned two international awards. In 2024, his edited volume Data Science: Techniques and Intelligent Applications received the Choice Outstanding Academic Titles Award. He is also an ISO 27001:2022 Certified Lead Auditor and has served as guest faculty at institutions including National Taipei University, Taiwan, and UMA, Peru.
Sachin S. Bere is an associate professor in the Dattakala Group of Institutions Faculty of Engineering Bhigwan. He completed his Ph.D. in Computer Science and Engineering from the SJJT University, Rajasthan. He also completed his MTech (CSE) with First Class & Distinction from a JNTU-Hyderabad-affiliated college. He has 18 years of teaching experience and 7 years of research experience. He published almost 30 research articles in reputed journals and conferences. His areas of interest are machine learning, artificial intelligence, deep learning techniques, and programming languages.
Piyush P. Gawali is an Assistant Professor in the Department of Computer Engineering at Vishwakarma Institute of Information Technology, Pune, India. Mr. Piyush Prabhat Gawali earned his M.E. in computer science and engineering and is pursuing a Ph.D. from Savitribai Phule Pune University. He has more than 16 years of teaching experience. His research interests include quantum computing, cybersecurity, medical cyber-physical systems, machine learning, and network security. He has more than 13 years of teaching experience and two years and six months of industry experience. He has 8 patents, 14+ research publications, and has authored/edited 2+ books with local and international publishers.
Content
1: Ant Colony Optimization and Grey Wolf Optimization - A Comparative Study for Healthcare Resource Allocation During COVID-19 Across States in India 2: AI-Based Decision Support Systems for Personalized Maternal Health Management Before Pregnancy 3: Advances in Deep Neural Networks for Chronic Kidney Disease Diagnosis: A Systematic Review 4: Beyond Crystal Balls: Machine Learning's Role in Proactive Healthcare - Predicting and Preventing Disease Outcomes 5: Unveiling the Veil: A Comprehensive Exploration of Interpretable Machine Learning for Healthcare and its Role in Elevating Transparency in Decision Support 6: A Comprehensive Exploration of How Deep Learning is Revolutionizing Patient Care in the Healthcare Landscape 7: Smart Healthcare Ecosystems: A Deep Dive into Applications, Advancements, and Ethical Considerations of Deep Learning Technologies 8: Healing Intelligence: A Deep Dive into the Cognitive Revolution of Healthcare through Advanced Deep Learning Technologies 9: Innovating at the Nexus: Unravelling the Impact of Deep Learning on Healthcare and Its Transformative Effect on Patient-Centric Solutions and Clinical Decision Support 10: Enhancing Healthcare Data Governance and Security: The Role of Adaptive Data Management Middleware in Federated Cloud Environments 11: Skin Cancer Detection Using U-Net 12: Revolutionizing Heart Disease Diagnosis using Machine Learning: A Case Study in Data-Driven Health care 13: Predicting drug response using Deep Learning techniques 14: Multimodal PCOS Detection: Combining XG Boost for Images with Zero Shot Learning for Textual Data 15: Revolutionizing Healthcare: Leveraging Fine-Tuned Large Language Models for Personalized Question-Answering Chatbots 16: Best Donor Selection for Liver Transplantation Using Artificial Neural Network and Machine Learning Algorithms 17: Clinical Decision Support Systems in Pre-Pregnancy Health: A Comparative Review of Traditional, Machine Learning, and Deep Learning Techniques 18: From Traditional Diagnostics to AI Innovations: A Comparative Study for Early Detection and Management of Chronic Kidney Disease 19: Deep Learning in Medical Imaging for Intracranial Hemorrhage Detection and Segmentation 20: Enhancing Healthcare Resource Allocation: An Insights for Research
