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Swarm Intelligence and Its Applications in Biomedical Informatics discusses Artificial Intelligence (AI) applications in medicine and biology, as well as challenges and opportunities presented in these arenas. It covers healthcare big data analytics, mobile health, personalized medicine, and clinical trial data management. This book shows how AI can be used for early disease diagnosis, prediction, and prognosis, and it offers healthcare case studies that demonstrate the application of AI and Machine Learning. Key Features: . Covers all major topics of swarm intelligence research and development such as novel-based search methods and novel optimization algorithm: applications of swarm intelligence to management problems and swarm intelligence for real-world application. . Provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up 'intelligent bioinformatics'. . Covers a wide range of topics on the role of AI, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. . Explores applications in different areas of healthcare and highlights the current research. This book is designed as a reference text, and it aims primarily at advanced undergraduates and postgraduate students studying computer science and bioinformatics. Researchers and professionals will find this book useful.
A. Sheik Abdullah is an Assistant Professor senior, at the School of Computer Science Engineering, Vellore Institute of Technology, in Chennai, Tamil Nadu, India. His research interests include Medical Data Research and Analytics, E-Governance, and Big Data. He has published books, written book chapters, and numerous journal articles.
1. Introduction. 2. Statistical Methods and Swarm Intelligence Techniques in Clinical Data Prediction. 3. Data Classification by Decision Trees - An Illustration . 4. Predictive Analytics Using Ant Colony Optimization with Decision Trees for Medical Data. 5. Predictive Analytics Using Bee-Based Harmony Search with Decision Trees for Medical Data. 6. Predictive Analytics Using Particle Swarm Optimization with Decision Trees for Type II Diabetes. 7. Case-Based Analysis in Medical Informatics. 8. Intelligent Optimization Unit. 9. Conclusion.
