Technology today is revolutionizing everything with the healthcare domain also seeing a major move towards automation. Intelligent systems assist doctors in diagnosing and giving prognosis with good accuracy and at a large scale. This new book, Data-Driven Analytics for Healthcare: Artificial Intelligence and Machine Learning for Medical Diagnostics, provides insight into various solutions for the diagnosis of patients with the help of automated processes using artificial intelligence techniques and technology.
This book highlights the possibilities of artificial intelligence, machine learning, and deep learning techniques and various tools to correctly diagnose various medical problems. It explores the opportunities in the data-driven analytics for healthcare in terms of research conducted in this domain and how the data in various healthcare-related applications can be handled and used. General predictive analysis methods are explained in various disease detection applications along with machine learning techniques for the implementation of predictive analysis methods. How the human brain can be used as a quantum computer for biomimetics and the mind-machine interface are also explained. The book covers object detection approaches for diseases such as pneumonia, application of monitoring and tracking the coronavirus disease, various endodontic applications for handling diagnostic abnormalities using deep learning methods, and applications of healthcare for human activity recognition in detail. It also studies the applications of artificial intelligence and machine learning for various medical applications, including MRI, X-ray, CT scan images and diagnosis, patient history data for diseases prediction, prescription of various drugs, and more.
This book will be of interest to computer scientists, electronics engineers, and bioinformatics researchers, pharmaceuticals professionals, and medical doctors, biotech professionals, and others in understanding the use and adoption of artificial intelligence and machine learning in healthcare and medicine.
Discusses various solutions for the diagnosis of patients with the help of automated processes using artificial intelligence techniques and technology. Highlights the possibilities of artificial intelligence, machine learning, and deep learning techniques and various tools to correctly diagnose various medical problems.
Introduction
1. Predictive and Descriptive Analytics in Healthcare
2.
Biomimetics and Mind-Machine Interface: The Human Brain as a Quantum Computer
3. The Inevitable Artificial Intelligence Revolution in Healthcare
4. Object
Detection Approach for Pneumonia Detection Using X-Ray Images
5. Corona
Tracker: An Application for Monitoring and Tracking Corona Disease
6.
Investigation on Diagnosing Irregularities in Endodontic Applications Using
Deep Learning Methods
7. TeknomoFernandez Kernelized Discriminant
Analysis-Based Connectionist Deep Multilayer Perceptive Neural Learning for
Human Activity Recognition
8. Transforming Healthcare with AI: An Adequate
Method for Diabetes Prediction Using Machine Learning Techniques
9. A Survey
on Artificial Intelligence and Machine Learning Approaches for Medical Data
Authentication
10. Data-Driven Analytics for Healthcare: Artificial
Intelligence and Machine Learning for Medical Diagnostics
11. M-Blockchain: A
Futuristic Approach for Healthcare
12. Blockchain in Healthcare: Evolution,
Applications, and Challenges
Meghna Sharma, PhD, is an Associate Professor and Data Science Specialisation Lead in the Department of Computer Science and Engineering at The NorthCap University, Gurugram, India. She has more than 20 years of teaching, research, and administration experience. She earlier worked as a scientist in the Control Systems Group, ISRO Satellite Center, Bangalore. She is a recipient of Award for Science conferred by Govt. of Haryana. She has published 35 research papers and three international patents and two national patents in the AI domain.
Priyanka Vashisht, PhD, is an Associate Professor of Computer Science and Engineering at Amity University Haryana, Gurugram, India. She has more than 19 years of teaching, research, and administration experience. She is a faculty sponsor for the ACM Students Chapter. She has published many research papers in journals and conferences as well as book chapters. She has two Indian and two international patents in her name. She has chaired international conferences and is a reviewer for many national and international conferences and journals.
A. V. Senthil Kumar, PhD, has five years of industrial experience and 27 years of teaching experience. He has published book chapters, papers in international and national journals and conferences, and several edited books. He is an Associate Editor of IEEE Access and a key member for the Machine Intelligence Research Lab, India. Professor Kumar is an editorial board member and reviewer for various international journals. He is also a committee member for various international conferences.
Chitra Singh, PhD, is a Professor of Chemistry Education and Environmental Education, National Council of Educational Research and Training, Bhopal, Madhya Pradesh, India, for more than 22 years. Dr. Singh has published more than 40 papers in various journals, completed eight research projects, conducted 25 training programs, and developed 20 curriculum materials.
Abdelmalek Amine, PhD, is a Professor of Computer Science at the University of Saida Dr. Moulay Tahar, Algeria, where he is also Director of the Knowledge Management and Complex Data Laboratory and Vice Rector of External Relations and Cooperation. His research interests include big data, IoT, data mining, text mining, ontology, classification, clustering, neural networks, and biomimetic optimization methods. He has several publications in the field of AI and is on the editorial boards of eminent international journals.
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