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Data-Driven Analytics for Healthcare: Artificial Intelligence and Machine Learning for Medical Diagnostics

Editat de Meghna Sharma, Priyanka Vashisht, A. V. Senthil Kumar, Chitra Singh, Abdelmalek Amine
en Limba Engleză Hardback – 10 feb 2025
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.
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Specificații

ISBN-13: 9781774917602
ISBN-10: 1774917602
Pagini: 290
Ilustrații: 70
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: Apple Academic Press Inc.
Colecția Apple Academic Press

Public țintă

Academic and Postgraduate

Cuprins

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. Teknomo–Fernandez 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

Notă biografică

Meghna Sharma, PhD, is currently working as Associate Professor & Data Science Specialisation Lead in the Department of Computer Science and Engineering at The NorthCap University (NCU), Gurugram, India. She has more than 20 years of teaching, research, and administration experience. Dr. Sharma has 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. Her major areas of interests are machine learning, artificial intelligence, big data analytics, and data mining. She has more than 35 research papers to her credit published in reputed journals, conferences, and book chapters. She has three international patents published and granted and two national patents published in the AI domain. She has also been a participant in the program committees of several international conferences and on the editorial boards of eminent international journals. She has many certification courses of cutting-edge technologies in the domain of data science to her credit from international and national universities of repute through NPTEL, Coursera, and UpGrad. She has obtained her Doctor of Philosophy in area of Machine Learning from J.C. Bose University of Science and Technology, YMCA, India.
Priyanka Vashisht, PhD, is currently working as an Associate Professor in the Department of Computer Science & Engineering at Amity University Haryana, Gurugram, India. She earned her doctorate degree in Grid Computing from Thapar University, Patiala, India, in 2017. Her major areas of interests are cloud computing, fog computing, machine learning, big data analytics, and data mining. She has more than 19 years of teaching, research, and administration experience. She is a member of the Association for Computing Machinery (ACM) and faculty sponsor for the ACM Students Chapter. She has numerous research papers to her credit published in reputed journals, conferences, as well as book chapters. She has two Indian and two international patents in her name. She is associated to several computer science journals. She has chaired various international conferences and is a reviewer for many national and international conferences and journals.
A. V. Senthil Kumar, PhD, DSc, to his credit he has industrial experience of five years and teaching experience of 27 years. He has received his Doctor of Science (DSc in Computer Science). He has to his credit 33 book chapters, 220 papers in international and national journals 60 papers in international and national conferences, and edited 12 books (IGI Global, USA). He is as Associate Editor of IEEE Access. He is an Editor-in-Chief for many journals and a key member for Machine Intelligence Research Lab (MIR Labs), India. Professor Kumar is an editorial board member and reviewer for various international journals. He is also a committee member for various international conferences. He is a life member of the International Association of Engineers (IAENG) and Systems Society of India (SSI) and a member of The Indian Science Congress Association, Internet Society (ISOC), International Association of Computer Science and Information Technology (IACSIT), Indian Association for Research in Computing Science (IARCS), and committee member for various international conferences.
Chitra Singh, PhD, has been working as a Professor in the Department of Chemistry Education and Environmental Education, National Council of Educational Research and Training (NCERT), 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 full Professor of Computer Science at the University of Saida Dr. Moulay Tahar, Algeria, where he also Director of the Knowledge Management and Complex Data Laboratory (GeCoDe Lab) andVice 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 (articles, chapters, and edited books) in the field of AI. He has also been a participant in the program committees of several international conferences and on the editorial boards of eminent international journals.

Descriere

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.