By M. Sordo, S. Vaidya, L. C. Jain (auth.), Dr. Margarita Sordo, Dr. Sachin Vaidya, Prof. Lakhmi C. Jain (eds.)
Advanced Computational Intelligence (CI) paradigms are more and more used for enforcing powerful laptop functions to foster security, caliber and efficacy in all points of healthcare. This study ebook covers an plentiful spectrum of the main complicated functions of CI in healthcare.
The first bankruptcy introduces the reader to the sphere of computational intelligence and its functions in healthcare. within the following chapters, readers will achieve an realizing of powerful CI methodologies in numerous very important issues together with medical choice help, selection making in medication effectiveness, cognitive categorizing in scientific info process in addition to clever pervasive healthcare structures, and agent middleware for ubiquitous computing. chapters are dedicated to imaging functions: detection and class of microcalcifications in mammograms utilizing evolutionary neural networks, and Bayesian tools for segmentation of clinical pictures. the ultimate chapters conceal key elements of healthcare, together with computational intelligence in track processing for blind humans and moral healthcare agents.
This e-book could be of curiosity to postgraduate scholars, professors and practitioners within the parts of clever structures and healthcare.
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Extra info for Advanced Computational Intelligence Paradigms in Healthcare - 3
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A performance measure, such as the accuracy, is computed to quantitatively summarize the eﬃcacy of the system. In two-class classiﬁcation problems, Receiver Operating Characteristic (ROC) analysis is widely used for analyzing the classiﬁer performance . , healthy and disease. By varying the threshold, a ROC curve of sensitivity versus (1-speciﬁcity) is generated. The area under the ROC curve (AUC) is often used as a metric to quantitatively summarize the performance of a clinical decision support system.