Advanced Computational Intelligence Paradigms in Healthcare by M. Sordo, S. Vaidya, L. C. Jain (auth.), Dr. Margarita

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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Electroencephalogram processing using neural networks. Clinical Neurophysiology, 2002. 50. M. Sordo, H. Buxton, D. Watson. A Hybrid Approach to Breast Cancer Diagnosis. In Practical Applications of Computational Intelligence Techniques. Jain, L. and DeWilde, P. ). Kluwer Academic Publishers. 2001. 51. N. S. Joo. Development of a fuzzy logic based system to monitor the electrical responses of nerve fiber. Biomed Sci Instrum 1997;33:376–81. 52. H. Allum, F. Honegger, M. Troescher. Principles underlying real-time nystagmus analysis of horizontal and vertical eye movements recorded with electro-, infra-red-, or video-oculographic techniques.

Waschulzik, W. Brauer et al. Segmentation of Computertomographies with Neural Networks Based on Local Features. , & Rosen, K. ), International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, pp. 240–247. 1994. 42. T. Olmez, Z. Dokur. Classification of heart sounds using an artificial neural network. Pattern Recognition Letters. Vol. 24, Issue 1–3 pp. 617–629. January 2003. 43. T. Olmez, Z. Dokur. Application of InP Neural Network to ECG Beat Classification. Neural Computing Applications Journal.

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 [45]. , 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.

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