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Neuro fuzzy hybrid models for classi...

Guzman, Juan Carlos.

Neuro fuzzy hybrid models for classification in medical diagnosis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neuro fuzzy hybrid models for classification in medical diagnosisby Patricia Melin, Juan Carlos Guzman, German Prado-Arechiga.
Author:	Melin, Patricia.
other author:	Guzman, Juan Carlos.
Published:	Cham :Springer International Publishing :2021.
Description:	ix, 103 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-3-030-60481-3
ISBN:	9783030604813$q(electronic bk.)

Neuro fuzzy hybrid models for classification in medical diagnosis
Melin, Patricia.

Neuro fuzzy hybrid models for classification in medical diagnosis[electronic resource] /by Patricia Melin, Juan Carlos Guzman, German Prado-Arechiga. - Cham :Springer International Publishing :2021. - ix, 103 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology. - SpringerBriefs in applied sciences and technology..

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.

ISBN: 9783030604813$q(electronic bk.)

Standard No.: 10.1007/978-3-030-60481-3doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / M45 2021

Dewey Class. No.: 610.28563

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