1st Edition

Machine Learning for Healthcare Systems Foundations and Applications

    250 Pages 86 Color & 13 B/W Illustrations
    by River Publishers

    250 Pages 86 Color & 13 B/W Illustrations
    by River Publishers

    The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the world still lacks a fully integrated healthcare system. The intrinsic complexity and development of human biology, as well as the differences across patients, have repeatedly demonstrated the significance of the human element in the diagnosis and treatment of illnesses. But as digital technology develops, healthcare providers will undoubtedly need to use it more and more to give patients the best treatment possible.

    The extensive use of machine learning in numerous industries, including healthcare, has been made possible by advancements in data technologies, including storage capacity, processing capability, and data transit speeds. The need for a personalized medicine or "precision medicine" approach to healthcare has been highlighted by current trends in medicine due to the complexity of providing effective healthcare to each individual. Personalized medicine aims to identify, forecast, and analyze diagnostic decisions using vast volumes of healthcare data so that doctors may then apply them to each unique patient. These data may include, but are not limited to, information on a person’s genes or family history, medical imaging data, drug combinations, patient health outcomes at the community level, and natural language processing of pre-existing medical documentation.

    This book provides various insights into machine learning techniques in healthcare system data and its analysis. Recent technological advancements in the healthcare system represent cutting-edge innovations and global research successes in performance modelling, analysis, and applications.

    1. Investigation on Improving the Performance of Class Imbalanced Medical Health Datasets

    2. Improving Heart Disease Diagnosis using Modified Dynamic Adaptive PSO (MDAPSO)

    3. Efficient Diagnosis and ICU Patient Monitoring Model

    4. Application of Machine Learning in Chest X-Ray Images

    5. Integrated Solution for Chest X-ray Image Classification

    6 Predicting Genetic Mutations Among Cancer Patients by Incorporating LSTM with Word Embedding Techniques

    7. Prediction of Covid-19 Disease using Machine Learning Based Models

    8. Intelligent Retrieval Algorithm using Electronic Health Records for Healthcare Systems

    9 Machine Learning-based Integrated Approach for Cancer Microarray Data Analysis

    10. Feature Selection/Dimensionality Reduction

    11. Information Retrieval using Set-based Model Methods, Tools and Applications in Medical Data Analysis


    C. Karthik is an Associate Professor of Mechatronics Engineering at Jyothi Engineering
    College where he teaches courses on robotics and automation, mechatronics system design, and optimization algorithms. He has 12 years university-level teaching experience in electrical and computer engineering and has a strong CV about research activities in control system design and artificial intelligence. He holds a large number of patents and has received several medals and awards due to his innovative work and research activities. He is guest editor and editorial board member for many journals and has published several international journals papers. Karthik has also made several conference presentations and worked on the development and evaluation of several interactive computing projects. His research interests include the time delay control problem, nonlinear system identification, robotics and autonomous systems, sensor design and unmanned vehicles. He was recently involved in the research of sensor design for medical autonomous systems using machine learning techniques. Karthik is a member of the Association for Computing Machinery (ACM), the ACM Special Interest Group on Computer Human Interaction (SIGCHI), a senior member of IEEE, and a member of the IEEE Robotics and Automation Society.

    M. Rajalakshmi received her Ph.D. degree from Anna University, in 2020, in the area of system identification and controller tuning. She received her B.Eng. degree in electronics and instrumentation engineering from the Kamaraj College of Engineering and Technology, in 2010, and M.Tech. degree in instrumentation and control engineering from the Kalasalingam Academy of Research and Education (KARE), in 2012. She is working as an Associate Professor with Jyothi Engineering College, Thrissur, Kerala. She has published several international journal and conference papers. Her professional interests focus on machine learning, artificial intelligence, linear and nonlinear control systems, system identification, and her current projects include modeling and controlling of nonlinear process (machine learning algorithms for biomedical and robotics).

    Dr. Sachi Nandan Mohanty is an Associate Professor in the Department of Computer Engineering at the College of Engineering Pune, India. He received his post doctorate from IIT Kanpur in the year 2019 and Ph.D. from IIT Kharagpur, India in the year 2015, with a HRD scholarship from the Government of India. He has edited 14 books in association with Springer, Wiley and CRC Press. His research areas include data mining, big data analysis, cognitive science, fuzzy decision making, the brain–computer interface, and computational intelligence. Professor Mohanty received three best paper awards during his Ph.D. at IIT Kharagpur, from an International Conference at Beijing, China, and the other at International Conference on Soft Computing applications organized by IIT Rookee in the year 2013. He was awarded a best thesis award by the Computer Society of India in the year 2015. He has published 42 papers in international journals of repute and has been elected as a fellow of the Institute of Engineers, IETE, and a senior member of the IEEE Computer Society Hyderabad chapter. He is also a reviewer for Journal of Robotics and Autonomous Systems (Elsevier), Computational and Structural Biotechnology Journal (Elsevier), Artificial Intelligence Review (Springer), and Spatial Information Research (Springer).

    Mr. Subrata Chowdhury is perusing M.Tech at the Sreenivasa Institute of Technology and Management Studies, Chittoor Andra Pradesh, India. He has edited 5 books in association of the CRC press and others. His research areas include data mining, big data, Machine learning, Quantum Computing, Fuzzy logic, AI, Edge Computing, Swarm Intelligence, Healthcare. He receive Awards and Nominations from the different National & International Science societies. He had published more then 50 papers in international and reputed journals. He has been the member of the IET and a member of the IEEE. He is also the reviewers for the IEEE Transactions, Elsevier’s, Springers. And Academic editor for the Hindwai journals. He has been invited as a keynote speakers for many Workshops, Conferences and Seminars.