dc.contributor.author | Özekes, Serhat | |
dc.contributor.author | Erguzel, Turker Tekin | |
dc.date.accessioned | 2016-04-06T07:49:36Z | |
dc.date.available | 2016-04-06T07:49:36Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Erguzel, T. T. & Ozekes, S. (2014) Artificial intelligence approaches in psychiatric disorders. The Journal of Neurobehavioral Sciences, 1 (2), 52-53. | tr_TR |
dc.identifier.issn | 2148-4325 | |
dc.identifier.uri | http://earsiv.uskudar.edu.tr/xmlui/handle/123456789/542 | |
dc.description.abstract | Potential utility of machine learning (ML) methods can be used as a clinical tool in administering diagnosis and therapy to a targeted group of subjects suffering from psychiatric diseae. The use of ML methodology is more potentially useful to the clinician as a classification or treatment response prediction tool . It is worth using feature selection algorithms to raise the sensitivity and accuracy of the models to contribute to the hybrid approach of artificial intelligence methodologies. | tr_TR |
dc.language.iso | eng | tr_TR |
dc.publisher | Üsküdar Üniversitesi | tr_TR |
dc.relation.isversionof | 10.5455/JNBS.1405259279 | tr_TR |
dc.subject | Machine learning | tr_TR |
dc.subject | feature selection | tr_TR |
dc.subject | psychiatric diseases | tr_TR |
dc.title | Artificial intelligence approaches in psychiatric disorders | tr_TR |
dc.title.alternative | PSİKİYATRİK BOZUKLUKLARDA YAPAY ZEKA YAKLAŞIMLARI | tr_TR |
dc.type | Other | tr_TR |
dc.relation.journal | The Journal of Neurobehavioral Sciences | tr_TR |
dc.contributor.department | Üsküdar University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering | tr_TR |
dc.identifier.volume | 1 | tr_TR |
dc.identifier.issue | 2 | tr_TR |
dc.identifier.startpage | 52 | tr_TR |
dc.identifier.endpage | 53 | tr_TR |
dc.contributor.authorID | TR29371 | tr_TR |
dc.contributor.authorID | TR19915 | tr_TR |