Electronics and Communications Technology

Machine Learning Applications for Data Analysis in Healthcare Systems
Editors: Sudeshna Chakraborty, PhD
Jyotsna Singh, PhD
Praveen Kumar Shukla, PhD
Prasenjit Chatterjee, PhD

Machine Learning Applications for Data Analysis in Healthcare Systems

Published. Available now.
Pub Date: November 2025
Hardback Price: see ordering info
Hard ISBN: 9781779643186
E-Book ISBN: 9781779643193
Pages: 250pp w/index
Binding Type: Hardback / ebook
Notes: 10 color and 50 b/w illustrations

Machine Learning Applications for Data Analysis in Healthcare Systems is a comprehensive exploration of the powerful intersection between machine learning and healthcare. It investigates the ever-changing impact of machine learning techniques on data analysis in healthcare, transforming the way we approach medical challenges, improve patient outcomes, and enhance healthcare systems.

The healthcare industry generates an enormous amount of data, from electronic health records and medical imaging to genomic sequencing and wearable devices. However, the true value of this data lies not in its sheer volume but in the insights it can provide. Machine learning algorithms offer the means to unlock the hidden patterns and knowledge within this data, enabling us to make informed decisions, identify high-risk patients, and personalize interventions for better healthcare outcomes.

The book is organized into sections, each focusing on a specific aspect of machine learning applications in healthcare systems. It begins by investigating the application of machine learning in-hospital mortality among heart failure patients, machine learning and its potential in outbreak prediction, the design and development of anti-cancerous drug molecules, and also delves into heartbeat classification based on a machine-human interaction model. The book looks at the application of machine learning in clinical decision-making, predictive modeling, personalized medicine, genomics, and public health management.

Throughout the book, the authors emphasize the practical implementation of machine learning techniques, supported by real-world case studies and examples. They also address the ethical considerations and challenges associated with implementing machine learning in healthcare, ensuring that responsible and ethical practices are at the forefront of the discussions.

Machine Learning Applications for Data Analysis in Healthcare Systems provides the knowledge and tools necessary to navigate the exciting landscape where machine learning and healthcare converge. By understanding the principles, challenges, and practical examples presented in this book, readers will be empowered to leverage machine learning techniques effectively and contribute to the advancement of healthcare for the benefit of patients and society as a whole.

CONTENTS:
Preface

1. Classification of In-Hospital Mortality for Heart-Failure Patient Using a Resource Constraint Dataset

Diganta Sengupta, Subhash Mondal, Suvam Gupta, and Shivam Agarwal

2. Predicting the Pandemic Outbreak (Covid-19) Using Facebook Prophet
Keshav Kaushik

3. Mobile App for Analyzing and Predicting
Virendra Kushwah, Prachi Bhatt, Toshini Agrawal, Jigyasa Bisht, and Shivangi Singh

4. Machine Learning-Based Analytics for Premature Rheumatoid Arthritis and Osteoarthritis Detection in Clinical Practices: A Review
Ganesh Kumar M. and Agam Das Goswami

5. Application of Artificial Intelligence in the Healthcare Sector: Benefits and Challenges
Ram Singh, Rohit Bansal, and Niranjanamurthy M.

6. Design and Development of Anti-Cancerous Drug Molecules by Structure and Ligand-Based Drug Designing Computational Approaches
Pallavi Singh, Somya Sinha, Akash Srivastava, and Nivedita Upadhyay

7. Smart Healthcare Systems: An Exigency of Current Era
Anupama Sharma, Prashant Srivastava, Prateek Srivastava, Shweta Roy, and Sandhya Avasthi

8. Technology-Enabled Smart Healthcare Toward Smart Society 5.0
Chabi Gupta

9. ELM-HC: An Approach for Heartbeat Classification Based on a Machine-Human Interaction Model
Vipul Narayan, Pawan Kumar Mall, Swapnita Srivastava, Pallavi Jain, Vimal Kumar, Anjey Mani Tripathi, and Sudeshna Chakraborty

10. Bow-Tie Construction from Accident Narratives: A Text-Mining Approach
Ashish Garg, Souvik Das, Amardeep Kumar, and J Maiti

11. Finger Knuckle Print: an Emerging Person Recognition Trait for Online Applications
Brajesh Kumar Singh, Anil Kumar, and Sudeshna Chakraborty

12. Face Mask Detection Using Deep Learning and Image Processing Algorithms
Ajitesh Gautam, Ashish Yadav, Pallavi Goel, and Shantanu Singh


Index


About the Authors / Editors:
Editors: Sudeshna Chakraborty, PhD
Professor and Research Group Head (data analytics and deep learning) Department of Computer Science Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

Sudeshna Chakraborty, PhD, is currently a Professor and Research Group Head of data analytics and deep learning at the Department of Computer Science Engineering at Galgotias University, Greater Noida, Uttar Pradesh, India. She has over 20 years of rich academic and industry experience. She has acquired several awards as a distinguished professor, including a research excellence award, Engineers Award by the Institute of Engineers of India, amongst others. She has also been a keynote speaker, an organizing member of international conferences, a member of review committees, session chair with the Institute of Engineers, speaker at InSc- and AICTE Training and Learning (ATAL) Academy-sponsored and other faculty development programs, etc. She has filed eight patents in the field of robotic, solar energy, and sensors. She has chaired an IEEE conference in Paris, including International Conference on Advances in Computing and Communication Engineering (ICACCE) , International Conference on Innovative Computing and Communication (ICICC), International Conference on Cyber Security and Artificial Intelligence (ICCSAI), and International Conference on Structures and Architecture (ICSA2025) (ICS2A). Dr. Chakraborty has been instrumental in various industrial interfacing for both academics and researchers at her previous assignments at various organizations (Sharda University, ManavRachna University, Mumbai University, Lingaya’s, Institute of Chartered Financial Analysts of India [ICFAI], and others). She has over 150 publications in Scopus- and SCI-indexed high-impact journals and international conferences. She has published 20 patents, five of which have been granted. She is guiding five PhD scholars at various universities, two of whom have successfully completed their PhDs, along with several post- and undergraduate students. At the same time, she has earned prestigious accreditations from NAAC, NBA, QAA, WASC, UGC, IAU, IET, and others.  She holds MTech and PhD degrees in Computer Science and Engineering, and her area of expertise is neural network and semantic web engineering.

Jyotsna Singh, PhD
Director, SVKM’s NMIMS Chandigarh and Associate Dean, STME, NMIMS Chandigarh, India

Jyotsna Singh, PhD, is a BE, MTech, and PhD holder with over 22 years of experience in the education industry. She has held several senior academic and administrative positions such as Director, Deputy Dean Students, and more, across esteemed institutions, including NIT Kurukshetra, NorthCap University, Amity University, Lloyd Group, IILM, among others. She is currently serving as the Director (I/C), NMIMS Chandigarh, and Associate Dean, School of Engineering and Technology, SVKM’s NMIMS Chandigarh Campus. Dr. Singh specializes in Computer Science and Engineering and holds professional certifications in data science, python, machine learning, artificial intelligence, computational thinking, and strategic mindset from reputed institutions such as Wipro, IBM, Deeplearning.AI, University of Pennsylvania, and University of Michigan. She also completed a certification in High Impact Teaching Skills organized by Wipro and attested by Dale Carnegie & Associates. In her illustrious career, she has led various workshops, undertaken government-funded projects, and launched several university-level initiatives. She has presented and published high-quality research in reputed journals and conferences and is proficient in programming languages such as C, C++, Python, among others. She has also filed over five patents in the areas of AI, IoT, and smart systems. An accomplished author, she has several departmental books to her credit, including Lab Manual on Software Project Management, FOCP, and Data Structures. Her contributions to the academic world were recognized when she received the “Torchbearer of Education” award in 2020 from Coding Ninjas.

Praveen Kumar Shukla, PhD
Associate Professor, Department of IOT & Intelligent Systems, Manipal University Jaipur, Jaipur, Rajasthan, India

Praveen Kumar Shukla, PhD, is an Associate Professor, Department of IOT & Intelligent Systems, Manipal University Jaipur, Jaipur, Rajasthan, India. He received his BTech degree from RGPV University, Bhopal, India, and his MTech degree in Control and automation from VIT Vellore, India. He earned his PhD in Brain-Computer Interfacing from NIT Raipur, India. He was a Postdoctorate Research Fellow in the Department of Computer Science Engineering at the Indian Institute of Technology (IIT), Gandhinagar from 2023 to 2024. Dr. Shukla’s research interests focus on brain computer interfacing, medical image processing, and robotics. He has published 40 research articles. He is currently supervising two PhD students. He has six patents to his name. He has received four best paper awards and a best thesis award. He is reviewer for the IEEE Journal of Biomedical and Health Informatics, Disability and Rehabilitation: Assistive Technology, IEEE Access, etc.

Prasenjit Chatterjee, PhD
Dean (Research and Consultancy), MCKV Institute of Engineering, West Bengal, India

Prasenjit Chatterjee, PhD, is currently Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has over 6400 citations 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; Frontiers of Mechanical and Industrial Engineering; and 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).




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