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Full Description
Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing.
Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it.
The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.
Contents
1. Advanced Optimization Techniques (Introduction) 2. Metaheuristic Algorithms for Healthcare: Open Issues and Challenges 3. Metaheuristic-Based Augmented Multilayer Perceptrons for Cancer and Heart Disease Predictions 4. The Role of Metaheuristics in Multilevel Thresholding Image Segmentation 5. Role of Advanced Metaheuristics for DNA Fragment Assembly Problem. 6. Contribution of Metaheuristic Approaches for Feature Selection Techniques 7. Advanced Metaheuristics for Task Scheduling in Healthcare IoT 8. Metaheuristics for Augmenting Machine Learning Models to Process Healthcare Data 9. Deep Learning Models to Process Healthcare Data: Introduction 10. Metaheuristics to Augment DL Models Applied for Healthcare System 11. Intrusion Detection System for Healthcare System Using Deep Learning and Metaheuristics.
