Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/2047
Title: | Different approaches of data and attribute selection on headache disorder | Authors: | Simić, Maja Banković Z. Simić, Dragan Simić, Maja |
Issue Date: | 1-Jan-2018 | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Abstract: | © Springer Nature Switzerland AG 2018. Half of the general population experiences a headache during any given year. Medical data and information in turn provide knowledge on which physicians base their decisions and actions but, in general, it is not easy to manage them. It becomes increasingly necessary to extract useful knowledge and make scientific decisions for diagnosis and treatment of this disease from the database. This paper presents comparison of data and attribute selected features by automatic machine learning methods and algorithms, and by diagnostic tools and expert physicians, almost all from the last decade. | URI: | https://open.uns.ac.rs/handle/123456789/2047 | ISBN: | 9783030034955 | ISSN: | 3029743 | DOI: | 10.1007/978-3-030-03496-2_27 |
Appears in Collections: | FTN Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
5
checked on May 3, 2024
Page view(s)
20
Last Week
11
11
Last month
0
0
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.