Machine Learning Applications for Data Analysis in Healthcare Systems

This comprehensive exploration investigates the powerful intersection and 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. This volume emphasizes the practical implementation of machine learning techniques, supported by real-world case studies and examples.

Sudeshna Chakraborty, PhD, is a Professor and Research Group Head of data analytics and deep learning at Galgotias University, India. With over 20 years of academic and industry experience, she has received several awards. She has been a keynote speaker, an organizing member of international conferences, a member of review committees, session chair, speaker at training and faculty development programs, etc. She has filed eight patents in the field of robotic, solar energy, and sensors and has published in Scopus- and SCI-indexed journals and international conferences.

Jyotsna Singh, PhD, is Chairperson of the School of Technology Management and Engineering at NMIMS, India. In her more than 21-year career in education, she has been Director, Dean of Students, etc., with institutions including NIT Kurukshetra, Northcap University, Amity University, Lloyd Group, IILM, and others. She has participated in workshops, has undertaken government-funded projects, and has initiated dozens of university-related programs. She has published and presented research papers in journals and conferences as well as several departmental books.

Praveen Kumar Shukla, PhD, is an Assistant Professor 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 and is a reviewer for the several IEEE journals. He is currently supervising PhD students. He has six patents to his name. He has received four best paper awards and a best thesis award.

Prasenjit Chatterjee, PhD, is Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has published over 130 research papers and has authored and edited more than 15 books on intelligent decision-making, supply chain management, optimization techniques, risk, and sustainability modeling and is also an editor for several book series. He has received numerous awards for his work. 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).