國立虎尾科技大學 |

Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis/ by Patricia Melin, Juan Carlos Guzmán, German Prado-Arechiga.
作者:	Melin, Patricia.
其他作者:	Prado-Arechiga, German.
面頁冊數:	IX, 103 p. 77 illus., 57 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Biomedical Engineering and Bioengineering. -
電子資源:	https://doi.org/10.1007/978-3-030-60481-3
ISBN:	9783030604813

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 Guzmán, German Prado-Arechiga. - 1st ed. 2021. - IX, 103 p. 77 illus., 57 illus. in color.online resource. - SpringerBriefs in Computational Intelligence,2625-3712. - SpringerBriefs in Computational Intelligence,.

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

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

1211019
Biomedical Engineering and Bioengineering.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis
LDR:02592nam a22003855i 4500 001 1046290
003 DE-He213
005 20211125115509.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030604813 $9 978-3-030-60481-3
024 7 $a 10.1007/978-3-030-60481-3 $2 doi
035 $a 978-3-030-60481-3
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Melin, Patricia. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 678025
245 1 0 $a Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis $h [electronic resource] / $c by Patricia Melin, Juan Carlos Guzmán, German Prado-Arechiga.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a IX, 103 p. 77 illus., 57 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computational Intelligence, $x 2625-3712
520 $a 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.
650 2 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 1 4 $a Computational Intelligence. $3 768837
650 0 $a Biomedical engineering. $3 588770
650 0 $a Computational intelligence. $3 568984
700 1 $a Prado-Arechiga, German. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1290979
700 1 $a Guzmán, Juan Carlos. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1349825
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030604806
776 0 8 $i Printed edition: $z 9783030604820
830 0 $a SpringerBriefs in Computational Intelligence, $x 2625-3704 $3 1254760
856 4 0 $u https://doi.org/10.1007/978-3-030-60481-3
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)