Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis

Abstract Background Several hematological indices have been already proposed to discriminate between iron deficiency anemia (IDA) and β‐thalassemia trait (βTT). This study compared the diagnostic performance of different hematological discrimination indices with decision trees and support vector mac...

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Autores principales:	Fakher Rahim, Anoshirvan Kazemnejad, Mina Jahangiri, Amal Saki Malehi, Kimiya Gohari
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Diagnosis Classification tree algorithms Hematological discrimination indices Iron deficiency anemia (IDA) β‐thalassemia trait (βTT) CRUISE tree algorithm Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/2703b030a3394dd7b1466e9e992c7ce6
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spelling	oai:doaj.org-article:2703b030a3394dd7b1466e9e992c7ce62021-11-14T12:29:17ZDiagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis10.1186/s12911-021-01678-51472-6947https://doaj.org/article/2703b030a3394dd7b1466e9e992c7ce62021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01678-5https://doaj.org/toc/1472-6947Abstract Background Several hematological indices have been already proposed to discriminate between iron deficiency anemia (IDA) and β‐thalassemia trait (βTT). This study compared the diagnostic performance of different hematological discrimination indices with decision trees and support vector machines, so as to discriminate IDA from βTT using multidimensional scaling and cluster analysis. In addition, decision trees were used to determine the diagnostic classification scheme of patients. Methods Consisting of 1178 patients with hypochromic microcytic anemia (708 patients with βTT and 470 patients with IDA), this cross-sectional study compared the diagnostic performance of 43 hematological discrimination indices with classification tree algorithms and support vector machines in order to discriminate IDA from βTT. Moreover, multidimensional scaling and cluster analysis were used to identify the homogeneous subgroups of discrimination methods with similar performance. Results All the classification tree algorithms except the LOTUS tree algorithm showed acceptable accuracy measures for discrimination between IDA and βTT in comparison with other hematological discrimination indices. The results indicated that the CRUISE and C5.0 tree algorithms had better diagnostic performance and efficiency among other discrimination methods. Moreover, the AUC of CRUISE and C5.0 tree algorithms indicated more precise classification with values of 0.940 and 0.999, indicating excellent diagnostic accuracy of such models. Moreover, the CRUISE and C5.0 tree algorithms showed that mean corpuscular volume can be considered as the main variable in discrimination between IDA and βTT. Conclusions CRUISE and C5.0 tree algorithms as powerful methods in data mining techniques can be used to develop accurate differential methods along with other laboratory parameters for the discrimination of IDA and βTT. In addition, the multidimensional scaling method and cluster analysis can be considered as the most appropriate techniques to determine the discrimination indices with similar performance for future hematological studies.Fakher RahimAnoshirvan KazemnejadMina JahangiriAmal Saki MalehiKimiya GohariBMCarticleDiagnosisClassification tree algorithmsHematological discrimination indicesIron deficiency anemia (IDA)β‐thalassemia trait (βTT)CRUISE tree algorithmComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diagnosis Classification tree algorithms Hematological discrimination indices Iron deficiency anemia (IDA) β‐thalassemia trait (βTT) CRUISE tree algorithm Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Diagnosis Classification tree algorithms Hematological discrimination indices Iron deficiency anemia (IDA) β‐thalassemia trait (βTT) CRUISE tree algorithm Computer applications to medicine. Medical informatics R858-859.7 Fakher Rahim Anoshirvan Kazemnejad Mina Jahangiri Amal Saki Malehi Kimiya Gohari Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
description	Abstract Background Several hematological indices have been already proposed to discriminate between iron deficiency anemia (IDA) and β‐thalassemia trait (βTT). This study compared the diagnostic performance of different hematological discrimination indices with decision trees and support vector machines, so as to discriminate IDA from βTT using multidimensional scaling and cluster analysis. In addition, decision trees were used to determine the diagnostic classification scheme of patients. Methods Consisting of 1178 patients with hypochromic microcytic anemia (708 patients with βTT and 470 patients with IDA), this cross-sectional study compared the diagnostic performance of 43 hematological discrimination indices with classification tree algorithms and support vector machines in order to discriminate IDA from βTT. Moreover, multidimensional scaling and cluster analysis were used to identify the homogeneous subgroups of discrimination methods with similar performance. Results All the classification tree algorithms except the LOTUS tree algorithm showed acceptable accuracy measures for discrimination between IDA and βTT in comparison with other hematological discrimination indices. The results indicated that the CRUISE and C5.0 tree algorithms had better diagnostic performance and efficiency among other discrimination methods. Moreover, the AUC of CRUISE and C5.0 tree algorithms indicated more precise classification with values of 0.940 and 0.999, indicating excellent diagnostic accuracy of such models. Moreover, the CRUISE and C5.0 tree algorithms showed that mean corpuscular volume can be considered as the main variable in discrimination between IDA and βTT. Conclusions CRUISE and C5.0 tree algorithms as powerful methods in data mining techniques can be used to develop accurate differential methods along with other laboratory parameters for the discrimination of IDA and βTT. In addition, the multidimensional scaling method and cluster analysis can be considered as the most appropriate techniques to determine the discrimination indices with similar performance for future hematological studies.
format	article
author	Fakher Rahim Anoshirvan Kazemnejad Mina Jahangiri Amal Saki Malehi Kimiya Gohari
author_facet	Fakher Rahim Anoshirvan Kazemnejad Mina Jahangiri Amal Saki Malehi Kimiya Gohari
author_sort	Fakher Rahim
title	Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
title_short	Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
title_full	Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
title_fullStr	Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
title_full_unstemmed	Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
title_sort	diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
publisher	BMC
publishDate	2021
url	https://doaj.org/article/2703b030a3394dd7b1466e9e992c7ce6
work_keys_str_mv	AT fakherrahim diagnosticperformanceofclassificationtreesandhematologicalfunctionsinhematologicdisordersanapplicationofmultidimensionalscalingandclusteranalysis AT anoshirvankazemnejad diagnosticperformanceofclassificationtreesandhematologicalfunctionsinhematologicdisordersanapplicationofmultidimensionalscalingandclusteranalysis AT minajahangiri diagnosticperformanceofclassificationtreesandhematologicalfunctionsinhematologicdisordersanapplicationofmultidimensionalscalingandclusteranalysis AT amalsakimalehi diagnosticperformanceofclassificationtreesandhematologicalfunctionsinhematologicdisordersanapplicationofmultidimensionalscalingandclusteranalysis AT kimiyagohari diagnosticperformanceofclassificationtreesandhematologicalfunctionsinhematologicdisordersanapplicationofmultidimensionalscalingandclusteranalysis
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Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis

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