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Healthcare Solutions Using Machine Learning and Informatics [Kietas viršelis]

Edited by (Pandit DeenDayal Energy University, India), Edited by , Edited by
  • Formatas: Hardback, 254 pages, aukštis x plotis: 234x156 mm, weight: 566 g, 70 Line drawings, color; 12 Line drawings, black and white; 14 Halftones, color; 8 Halftones, black and white; 84 Illustrations, color; 20 Illustrations, black and white
  • Išleidimo metai: 21-Oct-2022
  • Leidėjas: Auerbach
  • ISBN-10: 1032201983
  • ISBN-13: 9781032201986
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 254 pages, aukštis x plotis: 234x156 mm, weight: 566 g, 70 Line drawings, color; 12 Line drawings, black and white; 14 Halftones, color; 8 Halftones, black and white; 84 Illustrations, color; 20 Illustrations, black and white
  • Išleidimo metai: 21-Oct-2022
  • Leidėjas: Auerbach
  • ISBN-10: 1032201983
  • ISBN-13: 9781032201986
Kitos knygos pagal šią temą:
"Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for the healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, intelligent IoT-based solutions for medical image analysis, medical big data processing, disease predictions. Machine learning and artificial intelligence use cases in healthcare presentedin the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence. The wide variety of topics covered include: Artificial Intelligence in healthcare Machine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19 Big data analytics solutions for healthcare data processing Reliable biomedical applications using AI models Intelligent IoT in healthcare. The book explains fundamental concepts as well as the advanced use cases illustrating how to apply emerging technologies such as machine learning, AI models, data informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain"--

Focuses on fundamental concepts of gathering, processing, analyzing the dataset from rich healthcare and biomedical sources. Covers interdisciplinary techniques such as data science, deep learning, statistics, big data analytics, smart devices, computer vision and IoT.
1. Introduction to Artificial Intelligence in Healthcare
2. Machine
Learning in Radio Imagining
3. Solutions Using Machine Learning for Diabetes
4. A Highly Reliable Machine Learning Algorithm for Cardiovascular Disease
Prediction
5. Machine Learning Algorithm for Industry Using Image Sensing
6.
Solutions Using Machine Learning For COVID-19
7. Big Data Analytics in
Healthcare Data Processing
8. Reliable Biomedical Applications Using AI
Models
9. Disease Detection Using Imaging Sensors, Deep Learning and Machine
Learning for Smart Farming
10. IoT Application for Healthcare
11. Machine
Learning Algorithm for Diabetes Disease Prediction
12. Use of Machine
Learning in Healthcare
Dr. Punit Gupta is an Associate Professor in the Department of Computer and Communication Engineering at Manipal University, Jaipur, India.

Dr. Dinesh Kumar Saini is a Professor in the Department of Computer and Communication Engineering at Manipal University, Jaipur, India.

Dr. Rohit Verma is affiliated with the INSIGHT Research Lab SFI, Dublin City University, Dublin, Ireland.