Comparison of morphometric parameters in prediction of hydrocephalus using random forests

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buir.contributor.authorAlgın, Oktay
dc.citation.volumeNumber116en_US
dc.contributor.authorÖzgöde-Yigin, B.
dc.contributor.authorAlgın, Oktay
dc.contributor.authorSaygılı, G.
dc.date.accessioned2021-02-20T20:41:30Z
dc.date.available2021-02-20T20:41:30Z
dc.date.issued2020-01-01
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.description.abstractVentricles of the human brain enlarge with aging, neurodegenerative diseases, intrinsic, and extrinsic pathologies. The morphometric examination of neuroimages is an effective approach to assess structural changes occurring due to diseases such as hydrocephalus. In this study, we explored the effectiveness of commonly used morphological parameters in hydrocephalus diagnosis. For this purpose, the effect of six common morphometric parameters; Frontal Horns' Length (FHL), Maximum Lateral Length (MLL), Biparietal Diameter (BPD), Evans' Ratio (ER), Cella Media Ratio (CMR), and Frontal Horns’ Ratio (FHR) were compared in terms of their importance in predicting hydrocephalus using a Random Forest classifier. The experimental results demonstrated that hydrocephalus can be detected with 91.46 % accuracy using all of these measurements. The accuracy of classification using only CMR and FHL reached up to 93.33 %. In terms of individual performances, CMR and FHL were the top performers whereas BPD and FHR did not contribute as much to the overall accuracy.en_US
dc.embargo.release2021-01-01
dc.identifier.doi10.1016/j.compbiomed.2019.103547en_US
dc.identifier.issn0010-4825
dc.identifier.urihttp://hdl.handle.net/11693/75524
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://doi.org/10.1016/j.compbiomed.2019.103547en_US
dc.source.titleComputers in Biology and Medicineen_US
dc.subjectHydrocephalusen_US
dc.subjectMorphological parametersen_US
dc.subjectFeature importanceen_US
dc.subjectSemi-automatic analysisen_US
dc.titleComparison of morphometric parameters in prediction of hydrocephalus using random forestsen_US
dc.typeArticleen_US

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