Machine Learning in Healthcare
Advances and Future Prospects
1st Edition
Published on 15. September 2025
166 pages
978-1-040-42280-9 (ISBN)
Description
This new volume explores the integration of machine learning in healthcare, which has transformed technology for disease diagnosis, treatment, and management. The book shows the enormous possibilities made possible by computational technologies, ranging from analyzing electronic health information to predicting, detecting, and treating cancer, cardiovascular disease, thyroid disorders, and diabetes. The exploration extends beyond conventional domains, discussing topics such as wearable devices and mental health management through the use of machine learning technology.
Reviews / Votes
"Bridge[s] the gap between data science and medical practice, focusing on disease detection, personalized therapy, and holistic patient care. The book's visionary curation underscores the transformative potential of machine intelligence in shaping the future of healthcare delivery."-From the Foreword by Dhruv Galgotia, CEOm Galgotias University, Greater Noida, IndiaMore details
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
9 Line drawings, color; 1 Line drawings, black and white; 1 Halftones, black and white; 9 Illustrations, color; 2 Illustrations, black and white
File size
4,41 MB
ISBN-13
978-1-040-42280-9 (9781040422809)
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
Rishabha Malviya | Niranjan Kaushik | Tamanna Rai
Machine Learning in Healthcare
Advances and Future Prospects
Book
09/2025
1st Edition
Apple Academic Press
€210.50
Shipment within 3-4 weeks
Persons
Rishabha Malviya, PhD, is an Associate Professor of Pharmacy in the School of Medical and Allied Sciences at Galgotias University, India. He has authored over 150 research/review papers for national/international journals and holds over 50 patents (12 grants, 38 published, one filed). He has authored and/or edited 70 books as well as over 125 book chapters. He has been included in Stanford University's Top 2% Scientists list for the years 2020, 2021 and 2022.
Niranjan Kaushik, PhD, is a Professor of Pharmacy at Galgotias University's School of Medicine and Allied Sciences, Greater Noida, India. He has edited two books and has published research/review papers in national and international journals. He has been granted two patents, and four patents have been published and are undergoing assessment.
Tamanna Rai completed her BPharm at the Ram-Eesh Institute of Vocational and Technical Education, affiliated with Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow, and her MPharm at Galgotias University, Greater Noida, India. She has participated in numerous national and international conferences and has published manuscripts in international journals.
M. P. Saraswathy, MD, DNB, is a Clinical Microbiologist involved in diagnostic work and is an antimicrobial steward. She is an Associate Professor of Microbiology at ESIC Medical College, Chennai, India, where she is also the Nodal Officer for the ICTC-ART (Integrated Counseling & Testing Center-Anti- Retroviral Treatment) Centre. She is an executive council member of the Institute for Advanced Materials & Manufacturing-TN chapter. Dr. Saraswathy has several national and international publications to her credit.
Rajendra Awasthi, PhD, is an Associate Professor of Pharmaceutics at the School of Health Sciences, UPES University, Dehradun, India. He has over 17 years of professional experience. He has more than 160 publications to his credit, including book chapters. He has been included in Stanford University's Top 2% Scientists list. He has collaborated with renowned researchers from the University of Sao Paulo in Brazil, the University of Technology Sydney in Australia, and the University of Bath in England.
Niranjan Kaushik, PhD, is a Professor of Pharmacy at Galgotias University's School of Medicine and Allied Sciences, Greater Noida, India. He has edited two books and has published research/review papers in national and international journals. He has been granted two patents, and four patents have been published and are undergoing assessment.
Tamanna Rai completed her BPharm at the Ram-Eesh Institute of Vocational and Technical Education, affiliated with Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow, and her MPharm at Galgotias University, Greater Noida, India. She has participated in numerous national and international conferences and has published manuscripts in international journals.
M. P. Saraswathy, MD, DNB, is a Clinical Microbiologist involved in diagnostic work and is an antimicrobial steward. She is an Associate Professor of Microbiology at ESIC Medical College, Chennai, India, where she is also the Nodal Officer for the ICTC-ART (Integrated Counseling & Testing Center-Anti- Retroviral Treatment) Centre. She is an executive council member of the Institute for Advanced Materials & Manufacturing-TN chapter. Dr. Saraswathy has several national and international publications to her credit.
Rajendra Awasthi, PhD, is an Associate Professor of Pharmaceutics at the School of Health Sciences, UPES University, Dehradun, India. He has over 17 years of professional experience. He has more than 160 publications to his credit, including book chapters. He has been included in Stanford University's Top 2% Scientists list. He has collaborated with renowned researchers from the University of Sao Paulo in Brazil, the University of Technology Sydney in Australia, and the University of Bath in England.
Content
1. Machine Learning Algorithms in Disease Diagnosis and Management 2. Machine Learning-Based Diagnosis and Treatment of Cancer 3. Machine Learning-Based Detection and Management of Cardiovascular Diseases 4. Monitoring the Health Status of Thyroid Patients Using Machine Learning 5. Machine Learning-Based Wearable Devices for Healthcare Applications 6. Prediction of Diabetes Using Machine Learning 7. Mental Health Index Management Using Machine Learning 8. Machine Learning Approaches for Electronic Health Record Phenotyping
