Computational Intelligence for Machine Learning and Healthcare Informatics /
ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey.
- 1 online resource (XV, 331 p.)
- Intelligent Biomedical Data Analysis , 1 2629-7140 ; .
Frontmatter -- Preface -- Contents -- List of contributors -- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction -- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope -- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM -- 4. Computational intelligence approach to address the language barrier in healthcare -- 5. Recent advancement of machine learning and deep learning in the field of healthcare system -- 6. Predicting psychological disorders using machine learning -- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines -- 8. Machine learning approach for exploring computational intelligence -- 9. Classification of various image fusion algorithms and their performance evaluation metrics -- 10. Recommender system in healthcare: an overview -- 11. Dense CNN approach for medical diagnosis -- 12. Impact of sentiment analysis tools to improve patients' life in critical diseases -- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm -- 14. Machine learning in healthcare -- 15. Computational health informatics using evolutionary-based feature selection -- Index
This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.