Gebundenes Buch

Machine Learning Applications for Data Analysis in Healthcare Systems

Versandkostenfrei!

Erscheint vorauss. 18. November 2025

140,99 €

inkl. MwSt.

Jetzt bewerten

PAYBACK Punkte

70 °P sammeln!

Weiterlesen / Aufklappen

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.

Weniger Details

Sudeshna Chakraborty, PhD, is the Research Group Head of data analytics, web and mobile development and Professor at the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. With over 18 years of experience, she is an expert in neural network and semantic web engineering. She has been a keynote speaker, organizing member and reviewer for international conferences, session chair, etc. for conferences sponsored by the Institute of Engineers, the AICTE Training and Learning(ATAL) Academy Cell. She chaired the International Conference on Advances in Computing and Communication Engineering (ICACCE) and was a keynote speaker at a Springer conference in Tunisia (ICS2A) and a Track Chair for the International Conference on Smart Innovation, Ergonomics and Applied Human Factors. She has received several awards for her work, including a research excellence award by the NBSP (an online centralized database management system for a newborn sickle cell program in India) as well as a distinguished professor award. She has been instrumental in various industrial interfacing for academic and research at her previous assignments at various organizations. Dr. Chakraborty has published over 50 papers in international journals and conferences and has published 14 patents and copyrights and currently supervises several PhD students. Jyotsna Singh, PhD, is Chairperson of the School of Technology Management and Engineering at NMIMS, Chandigarh, India. In her more than 21-year career in education, she been Director, Dean of Students, etc., with esteemed institutions including NIT Kurukshetra, Northcap University, Amity University, Lloyd Group, IILM, and few others. She specializes in computer science and engineering and holds certificates in data science, Python, Computation Thinking, Strategic Mindset, and more from renowned organizations like Wipro, the University of Pennsylvania, University of Michigan, IBM etc. She has been the part of various workshops, has undertaken government-funded projects, and has initiated dozens of university-related programs and affairs. She has published and presented high quality research papers in reputed journals and conferences. She is proficient in computer languages such as C, C++, Python, and many others. Her publications also expand to the departmental books under her name, like Lab Manual on Software Project Management, FOCP, and Data Structures. Her most recent accolades include receiving an award as Torchbearer of Education in 2020 from Coding Ninjas. Dr. Singh has BE, MTech, and PhD degrees. Praveen Kumar Shukla, PhD, 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. Currently, he is working as Assistant Professor (Selection Grade) with the Department of IoT and Intelligent Systems at Manipal University Jaipur, India. 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, 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).