Articles liés à Improving Medical Data Classification Accuracy Using...

Improving Medical Data Classification Accuracy Using Ensemble methods: An Application of Ensemble Learning Methods - Couverture souple

Oskooi, Behzad; Rahati Quchani, Saeed

 
9783844358223: Improving Medical Data Classification Accuracy Using Ensemble methods: An Application of Ensemble Learning Methods
Couverture souple
ISBN 10 : 3844358226 ISBN 13 : 9783844358223
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Currently, electronic medical instruments are widely used in hospitals to gather vital information about patients’ bodies. This medical data is used by professionals to distinguish the causes of illnesses and the EEG is a well known test for this purpose. The EEG has many features and thus the volume of gathered data can increase dramatically if the number of samples or patients is enlarged. Interpreting this huge amount of data is not a simple task as the analysis is very time consuming. The application of machine learning methods can reduce this time for analysis as well as increase the accuracy of such analyses. In this publication, experiments are designed to take advantage of ensemble learning methods to classify large amounts of medical information which have many input vectors. The aim of these experiments is to differentiate between two groups of EEG data, collected during two separate Reiki and placebo operations. The analysis should help shed some light on this new and exciting field of study, and should be especially useful for professionals in the fields of Computer Science, or anyone else considering utilizing machine learning methods for clinical purposes.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Currently, electronic medical instruments are widely used in hospitals to gather vital information about patients’ bodies. This medical data is used by professionals to distinguish the causes of illnesses and the EEG is a well known test for this purpose. The EEG has many features and thus the volume of gathered data can increase dramatically if the number of samples or patients is enlarged. Interpreting this huge amount of data is not a simple task as the analysis is very time consuming. The application of machine learning methods can reduce this time for analysis as well as increase the accuracy of such analyses. In this publication, experiments are designed to take advantage of ensemble learning methods to classify large amounts of medical information which have many input vectors. The aim of these experiments is to differentiate between two groups of EEG data, collected during two separate Reiki and placebo operations. The analysis should help shed some light on this new and exciting field of study, and should be especially useful for professionals in the fields of Computer Science, or anyone else considering utilizing machine learning methods for clinical purposes.

Biographie de l'auteur

The Author graduated with a Masters in Computer Science from Uppsala University of Sweden. He is an expert in Siemens, Cisco and Huawei telecommunication systems in Telecom of Iran. He teaches Computer Security course in Institute of Telecom of Iran and also serves as lead auditor of ISO 27001 Security and ISO 9001:2001 standards.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3844358226
ISBN 13 
9783844358223
Reliure
Broché
Nombre de pages
108
Coordonnées du fabricant
non disponible
Personne responsable
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