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This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.
Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.
In the recent two decades, the acceptance of metaheuristics, particularly swarm intelligence and evolutionary algorithms (EAs), has exploded. Researchers offer a plethora of algorithms and approaches every year, and they discover a growing number of innovative applications. When we consider our daily lives, it becomes apparent that intelligent systems play a greater part in our work or practical actions, and numerous changes have been noted over the past several years in terms of intelligent approaches, methods, and techniques. Currently, the discipline of metaheuristics is having considerable influence on the spectrum of healthcare, particularly in illness diagnosis and medicine discovery. Genetic intelligence has evolved as a new-generation technique belonging to the evolutionary computing category. While evolutionary computation, especially when biologically inspired, may be effective for search and optimization, selecting an optimal solution from a search space that is often huge and/or complicated is very much in line with the natural evolution process. Typically, the underlying process of evolution is driven by a stochastic heuristic that is suitable to a given optimization environment. In genetic intelligence, the search for an optimal in a search space is defined by the way a swarm approaches its objective.
Chapter 1 deals with investigations on Mathematical Models of Evolutionary Intelligence. It starts by discussing about findings of various researchers from a variety of scientific and technical areas who have been investigating the capability of EAs. It contains primitive representation and primitive inference. This chapter briefly described the conceptual methodology behind evolutionary intelligence algorithms. Moreover, the basic structural components of EAs and the various types of EAs are discussed. Finally, the role of EA in healthcare is analyzed and summarized with respect to futuristic needs.
Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest