Short description:

In the last two decades, machine learning has been dramatically developed and is still experiencing a fast and ever-lasting change in paradigm, methodology, applications, and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on the diversity and complexity.

Long description:

In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity.

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges.

This book is useful for research scholars and students involved in critical condition analysis and computation models.

Table of Contents:

1. Machine Learning in Healthcare. 2. Feature Extraction and Applications of Bio Signals. 3. Machine Learning Methods for Managing Parkinson?s Disease. 4. Challenges of Medical Text and Image Processing. 5. Machine Learning Solutions in Computer-Aided Medical Diagnosis. 6. Rule Learning in Healthcare and Health Services Research. 7. Diagnosis in Medical Imaging. 8. Identifying Diseases and Diagnosis Using Machine Learning. 9. Machine Learning-Based Behavioral Modification. 10. Smart Health Records. 11. Treatment Recommendation System. 12. Smart Health Informatics System. 13. Natural Language Processing Utilization in Healthcare. 14. Clinical Decision Support and Predictive Analytics. 15. Bioinformatics and Biometrics. 16. Human Computer Interfaces and Usability. 17. Education and Capacity Building. 18. Learning Analytics for Competence Assessment. 19. Patient Simulators. 20. Serious Gaming. 21. Patient Empowerment and Engagement. 22. Social Media, Mobile Apps, and Patient Portals. 23. Human Factors and Technology Adoption. 24. Surveillance System. 25. Robotics. 26. Object Detection. 27. Traffic Analysis. 28. Big Data in Healthcare Systems. 29. Advanced Decision-Making and Data Analytics. 30. Emergence of Decision Support Systems. 31. Big Data Based Frameworks and Machine Learning. 32. Predictive Analysis and Modeling. 33. Security and Privacy with Machine Learning Systems. 34. Role of Social Media in Healthcare Analytics. 35. Big Data Based Case Studies for Healthcare Analytics. 36. Machine Learning and Deep Learning Paradigms and Case Studies. 37. Machine Learning in Agriculture.