Full Description
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.
Contents
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
