Data-Driven Diagnostics and Disease Prediction with AI Optimization
- 1 Edición - 6 de octubre de 2025
- Última edición
- Editores: Shailendra Pratap Singh, Prabhishek Singh, Manoj Diwakar, Vinayakumar Ravi
- Idioma: Inglés
Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applic… Leer más
Descripción
Descripción
Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.
Puntos claves
Puntos claves
- Provides a complete overview of the challenges and pain points in implementing AI-driven diagnostics and disease prediction within the healthcare industry
- Explores various edge computing applications in healthcare and the use of hardware acceleration for AI applications
- Presents a comprehensive resource that bridges the gap between AI and healthcare
De interès para
De interès para
Data Scientists, Machine Learning Engineers, and Researchers who want to understand how to use and optimize deep learning techniques in healthcare
Índice
Índice
1. Introduction AI in Healthcare using Machine Learning and Deep Learning
2. The Importance of Diagnostics and Disease Prediction for Real World Data Sets.
3. Neural Networks and Deep Learning Frameworks
4. Deep Learning Architectures for Healthcare and Types of Healthcare Data, Data Collection and Sources
5. Data Pre-processing and Cleaning , Handling Data Privacy and Security
6. Building Machine Learning Models: Supervised Learning for Diagnostics and Unsupervised Learning for Disease Prediction
7. Building Deep Learning Models: Convolutional Neural Networks (CNNs) for image analysis from Healthcare Sectors
8. Recurrent Neural Networks (RNNs) for time-series data Transfer learning and pretrained models
9. Natural Language Processing for Healthcare Texts
10. Predictive Modeling for Early Disease Detection
11. Telemedicine and Remote Diagnostics
12. Ensuring Patient Privacy, Informed Consent, Ethical and Regulatory Considerations
13. Future Trends and Innovations in Healthcare AI, Quantum Computing, and Edge Computing
14. Multimodal Data Fusion for Enhanced Diagnostics
2. The Importance of Diagnostics and Disease Prediction for Real World Data Sets.
3. Neural Networks and Deep Learning Frameworks
4. Deep Learning Architectures for Healthcare and Types of Healthcare Data, Data Collection and Sources
5. Data Pre-processing and Cleaning , Handling Data Privacy and Security
6. Building Machine Learning Models: Supervised Learning for Diagnostics and Unsupervised Learning for Disease Prediction
7. Building Deep Learning Models: Convolutional Neural Networks (CNNs) for image analysis from Healthcare Sectors
8. Recurrent Neural Networks (RNNs) for time-series data Transfer learning and pretrained models
9. Natural Language Processing for Healthcare Texts
10. Predictive Modeling for Early Disease Detection
11. Telemedicine and Remote Diagnostics
12. Ensuring Patient Privacy, Informed Consent, Ethical and Regulatory Considerations
13. Future Trends and Innovations in Healthcare AI, Quantum Computing, and Edge Computing
14. Multimodal Data Fusion for Enhanced Diagnostics
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 20 de octubre de 2025
- Idioma: Inglés
Sobre los editores
Sobre los editores
SS
Shailendra Pratap Singh
SHAILENDRA PRATAP SINGH is currently working as an School of Computer Science Engineering and Technology at Bennett University, Greater Noida-201310, U.P., India. Shailendra Pratap Singh received his Ph.D. degree in Computer Science and Engineering from MNNIT Allahabad, Prayagraj, U.P., in 2017. He is currently working as an Associate Professor in the School of Computer Science, Engineering, and Technology at Bennett University, Greater Noida, U.P., India. He is the author of more than 45 articles and has invented more than two inventions. His research interests include optimization, software engineering, machine learning, and IoT applications.
Afiliaciones y experiencia
Associate Professor, School of Computer Science, Engineering, and Technology, Bennett University, Greater Noida, U.P., IndiaPS
Prabhishek Singh
Dr. Prabhishek Singh is Assistant Professor in the School of Computer Science Engineering and Technology at Bennett University, Greater Noida, India, where he has served since 2022. His academic and research activities focus on image processing, computer vision, machine learning, and deep learning. Dr. Singh serves as a Senior Member of IEEE and contributes to the scholarly community through editorial and peer-review roles for a range of journals and conferences. His professional service includes appointments as Associate Editor, Academic Editor, Review Editor, Guest Editor, Reviewer, and Editorial Committee Chair. His research interests encompass the development and application of computational methods for intelligent image analysis and data-driven systems.
Afiliaciones y experiencia
Bennett University, Uttar Pradesh, IndiaMD
Manoj Diwakar
Dr. Manoj Diwakar is Associate Professor in the Department of Computer Science and Engineering at Graphic Era Deemed to be University, Dehradun, India. His research interests include image processing, information security, and medical imaging. He has contributed to research published in peer-reviewed journals, conference proceedings, books, and book chapters. Dr. Diwakar has served as a guest editor for scholarly journals and as a member of editorial boards in his areas of expertise. He has also been involved in organizing and supporting international conferences and research-focused academic initiatives. His work focuses on advancing interdisciplinary research and applications in computing and imaging technologies.
Afiliaciones y experiencia
Graphic Era Deemed to be University, Uttarakhand, IndiaVR
Vinayakumar Ravi
Dr. Vinayakumar Ravi is an Assistant Research Professor at the Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia. Dr. Ravi has been a Postdoctoral Research Fellow developing and implementing novel computational and machine learning algorithms and applications for big data integration and data mining with Cincinnati Children's Hospital Medical Center, Cincinnati, USA. He received his Ph.D. in Computer Science from Amrita School of Engineering, Coimbatore, India. His current research interests include applications of data mining, Artificial Intelligence, machine learning and, deep learning for biomedical informatics, cyber security, image processing, and natural language processing. Dr. Ravi is editor of Efficient Data Handling for Massive Internet of Medical Things: Healthcare Data Analytics, Springer. Dr. Ravi is an editorial board member for Journal of the Institute of Electronics and Computer (JIEC), International Journal of Digital Crime and Forensics (IJDCF), and he has organized a shared task force on detecting malicious domain names (DMD 2018) as part of SSCC'18 and ICACCI'18.
Afiliaciones y experiencia
Assistant Research Professor, Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi ArabiaVer libro en ScienceDirect
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