Machine Learning for Disease Detection, Prediction, and Diagnosis

Avita Katal

ISBN 10: 981964240X / ISBN 13: 9789819642403

Published by Springer Nature Singapore, Springer Nature Singapore Jun 2025, 2025

About this Item

Neuware -The book Machine Learning for Disease Detection, Prediction, and Diagnosis can be a comprehensive guide to the novel concepts, techniques, and frameworks essential for improving the viability of existing machine-learning practices. It provides an in-depth analysis of how these new technologies are helpful to detect, predict and diagnose diseases more accurately. The book covers various topics such as image classification algorithms, supervised learning methods like support vector machines (SVM), deep neural networks (DNNs), convolutional neural networks (CNNs), etc. unsupervised approaches such as clustering algorithms as well as reinforcement learning strategies.This book is an invaluable resource for anyone interested in machine-learning applications related to disease detection or diagnosis. It explains different concepts and provides practical examples of how they can it implements using real-world data sets from medical imaging datasets or public health records databases, among others. Furthermore, it offers insights into recent advances made by researchers which have enabled automated decision-making systems based on AI models with improved accuracy over traditional methods. This text also discusses ways through which current models could improve further by incorporating domain knowledge during the model training phase, thereby increasing their efficacy even further.Overall, this book serves as a great source of information about the latest advancements made in the field of Machine Learning & Artificial Intelligence towards efficient building systems capable enough detecting & diagnosing diseases automatically while avoiding human errors resulting due manual intervention at any stage along process pipeline thus ensuring better outcomes overall. Moreover, it helps readers understand the underlying principles behind each technique discussed so that they may apply them according to their own application scenarios efficiently without worrying much about the implementation details required to get the job done the right way the first time around itself! 404 pp. Englisch.

Seller Inventory # 9789819642403

Bibliographic Details

• Title: Machine Learning for Disease Detection, Prediction, and Diagnosis
• Publisher: Springer Nature Singapore, Springer Nature Singapore Jun 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 981964240X
• ISBN 13: 9789819642403
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 26D millimeters 241 millimeters width by 160 millimeters height by 26 millimeters depth
• Weight: 841 grams

About this title

Synopsis

The book “Machine Learning for Disease Detection, Prediction, and Diagnosis” can be a comprehensive guide to the novel concepts, techniques, and frameworks essential for improving the viability of existing machine-learning practices. It provides an in-depth analysis of how these new technologies are helpful to detect, predict and diagnose diseases more accurately. The book covers various topics such as image classification algorithms, supervised learning methods like support vector machines (SVM), deep neural networks (DNNs), convolutional neural networks (CNNs), etc. unsupervised approaches such as clustering algorithms as well as reinforcement learning strategies.

This book is an invaluable resource for anyone interested in machine-learning applications related to disease detection or diagnosis. It explains different concepts and provides practical examples of how they can it implements using real-world data sets from medical imaging datasets or public health records databases, among others. Furthermore, it offers insights into recent advances made by researchers which have enabled automated decision-making systems based on AI models with improved accuracy over traditional methods. This text also discusses ways through which current models could improve further by incorporating domain knowledge during the model training phase, thereby increasing their efficacy even further.

Overall, this book serves as a great source of information about the latest advancements made in the field of Machine Learning & Artificial Intelligence towards efficient building systems capable enough detecting & diagnosing diseases automatically while avoiding human errors resulting due manual intervention at any stage along process pipeline thus ensuring better outcomes overall. Moreover, it helps readers understand the underlying principles behind each technique discussed so that they may apply them according to their own application scenarios efficiently without worrying much about the implementation details required to get the job done the right way the first time around itself!

About the Author

Dr. Tanupriya Choudhury completed his undergraduate studies in Computer Science and Engineering at the West Bengal University of Technology in Kolkata (2004-2008), India, followed by a master’s degree in the same field from Dr. M.G.R University in Chennai, India (2008-2010). In 2016, he successfully obtained his PhD degree from Jagannath University, Jaipur. With a total of 15 years of experience in both teaching and research, Dr. Choudhury holds the position of visiting professor at Daffodil International University, Bangladesh. Currently, he is working as Professor in School of Computer Science, UPES, Dehradun.

Prior to this role, he served as a Professor and Associate Dean of Research at Graphic Era Deemed to be University, Dehradun, India, Professor at Symbiosis International Deemed University Pune, Graphic Era Hill University Dehradun (Research Professor), UPES Dehradun (Professor and Research Head Informatics), Amity University Noida (Assistant Professor Grade 3 and International Dept. Head), and other prestigious academic institutions (Dronacharya College of Engineering Gurgaon, Lingaya's University Faridabad, Babu Banarsi Das Institute of Technology Ghaziabad, Syscon Solutions Pvt. Ltd. Kolkata etc.). Recently recognized for his outstanding contributions to education with the Global Outreach Education Award for Excellence in Best Young Researcher Award at GOECA 2018.

His areas of expertise encompass Human Computing, Soft Computing, Cloud Computing, Data Mining among others. Notably accomplished within his field thus far is filing 25 patents and securing copyrights for 16 software programs from MHRD (Ministry of Human Resource Development). With more than 150 research papers (Scopus) authored to date on record, Dr. Choudhury has also been invited as a guest lecturer or keynote speaker at esteemed institutions such as Jamia Millia Islamia University, Maharaja Agersen College (Delhi University), Duy Tan University Vietnam etc. He has also contributed significantly to various conferences throughout India serving roles like TPC member and session chairperson.

Dr. Avita Katal is a highly regarded academic and researcher in the fields of Cloud Computing, Internet of Things (IoT), and Artificial Intelligence. She holds a Ph.D. in the domain of Cloud Computing, has completed her M.Tech and B.E. in Computer Science Engineering. Dr. Avita Katal is currently an Assistant Professor (Selection Grade) in the School of Computer Science at the University of Petroleum and Energy Studies (UPES) in Dehradun, Uttarakhand, India. She serves as the Program Leader for the B.Tech program in Computer Science & Engineering with  specialization in Cloud Computing and Virtualization Technologies at UPES. Dr. Avita Katal holds a Postgraduate Certificate in Academic Practice (PGCAP). With over a decade of research experience, Dr. Katal has contributed significantly to the development of advanced algorithms and systems in cloud computing environments, with particular focus on optimization techniques, resource management, and cloud security.

Dr. Katal has published extensively in reputed international journals and conferences, where her work has been recognized for its innovation and practical applications. She also serves as a reviewer for  prestigious journals and conferences in her field. Her ongoing research aims to bridge the gap between theoretical advancements and their implementation in real-world cloud infrastructures, particularly in the context of scalability, reliability, and efficiency.