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Data-Driven Diagnostics and Disease Prediction with AI Optimization de Shailendra Pratap Singh

Data-Driven Diagnostics and Disease Prediction with AI Optimization

Editat de Shailendra Pratap Singh, Prabhishek Singh, Manoj Diwakar, Vinayakumar Ravi
en Limba Engleză Paperback – oct 2025
Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. This 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. It offers 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.

  • 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
  • Provides a comprehensive resource that bridges the gap between AI and healthcare
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Specificații

ISBN-13: 9780443267475
ISBN-10: 0443267472
Pagini: 300
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

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