Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis (Paperback, 1st ed. 2021)

This book is focused on the use of intelligent techniques, such as fuzzy logic, neural networks and bio-inspired algorithms, and their application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible areas of the medicine and help to have an improvement in diagnosis accuracy considering a clinical monitoring of 24 hours or more of the patient. In this book, tests were made with different architectures proposed in the different modules of the proposed model. First, it was possible to obtain the architecture of the fuzzy classifiers for the level of blood pressure and for the pressure load, and these were optimized with the different bio-inspired algorithms (Genetic Algorithm and Chicken Swarm Optimization). Secondly, we tested with a local database of 300 patients and good results were obtained. It is worth mentioning that this book is an important part of the proposed general model; for this reason, we consider that these modules have a good performance in a particular way, but it is advisable to perform more tests once the general model is completed.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computational Intelligence
Release date:	October 2020
First published:	2021
Authors:	Patricia Melin • Juan-Carlos Guzman • German Prado-Arechiga
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	103
Edition:	1st ed. 2021
ISBN-13:	978-3-03-060480-6
Categories:	Books > Medicine > Nursing & ancillary services > Biomedical engineering > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-060480-2
Barcode:	9783030604806

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