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

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fakher Rahim ◽  
Anoshirvan Kazemnejad ◽  
Mina Jahangiri ◽  
Amal Saki Malehi ◽  
Kimiya Gohari
Keyword(s):  
Cluster Analysis ◽  
Support Vector Machines ◽  
Multidimensional Scaling ◽  
Diagnostic Performance ◽  
Classification Tree ◽  
Support Vector ◽  
Vector Machines ◽  
Tree Algorithms ◽  
And Cluster Analysis ◽  
Discrimination Methods

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.

scholarly journals Comparison of Diagnostic Performance of Classification Trees and Hematological Discrimination Functions for Differential Diagnosis between the Two Common Hematologic Disorders: An Application of Multidimensional Scaling and Cluster Analysis

2021 ◽  
Author(s):  
Fakher Rahim ◽  
Anoshirvan Kazemnejad ◽  
Mina Jahangiri ◽  
Amal Saki Malehi ◽  
Kimiya Gohari
Keyword(s):  
Cluster Analysis ◽  
Differential Diagnosis ◽  
Multidimensional Scaling ◽  
Diagnostic Performance ◽  
Classification Tree ◽  
Deficiency Anemia ◽  
Cross Sectional ◽  
Tree Algorithm ◽  
And Cluster Analysis ◽  
Discrimination Methods

Abstract Background: Several hematological indices have been already proposed to discriminate between iron deficiency anemia (IDA) and β‐thalassemia trait (βTT). The aim of the present study was to compare the diagnostic performance of different hematological discrimination indices with statistical methods such as decision trees to discriminate IDA from βTT.Methods: Consisting of 1178 patients with hypochromic microcytic anemia (708 patients with βTT and 470 patients with IDA), this cross-sectional study intended to compare the diagnostic performance of 43 hematological discrimination indices and tree-based methods such as J48, CART, Evtree, Ctree, QUEST, CRUISE as well as GUIDE to discriminate IDA from βTT. Moreover, multidimensional scaling and cluster analysis were used to identify the homogeneous subgroups of discrimination methods with similar performances.Results: All the classification tree algorithms showed acceptable accuracy measures for discrimination between IDA and βTT in comparison with other hematological discrimination indices. The results indicated that CRUISE tree algorithm had better diagnostic performance and efficiency among other discrimination methods. In turn, this tree algorithm showed the high Youden's index (88.03%), accuracy (94.57%), diagnostic odds ratio (311.63) and F-measure (95.54%) in the differential diagnosis of IDA from βTT. In addition, AUC of this algorithm indicated more precise classification with a value of 0.94 and this model was found to have excellent diagnostic accuracy. Also, CRUISE tree algorithm showed that Mean corpuscular volume can be considered as the main variable in discrimination as it extracted six homogenous subgroups of patients.Conclusions: CRUISE tree algorithm as a powerful method in data mining techniques can be used to develop accurate differential methods along with other laboratory parameters to discriminate IDA from βTT.

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

scholarly journals Taxonomy of Manufacturing Flexibility at Manufacturing Companies Using Imperialist Competitive Algorithms, Support Vector Machines and Hierarchical Cluster Analysis

2017 ◽  
Vol 7 (2) ◽  
pp. 1559-1566
Author(s):  
M. Khoobiyan ◽  
A. Pooya ◽  
A. Tavakkoli ◽  
F. Rahimnia
Keyword(s):  
Cluster Analysis ◽  
Support Vector Machines ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Support Vector ◽  
Manufacturing Flexibility ◽  
Manufacturing Companies ◽  
Validity Indices ◽  
Vector Machines ◽  
Competitive Algorithms

Manufacturing flexibility is a multidimensional concept and manufacturing companies act differently in using these dimensions. The purpose of this study is to investigate taxonomy and identify dominant groups of manufacturing flexibility. Dimensions of manufacturing flexibility are extracted by content analysis of literature and expert judgements. Manufacturing flexibility was measured by using a questionnaire developed to survey managers of manufacturing companies. The sample size was set at 379. To identify dominant groups of flexibility based on dimensions of flexibility determined, Hierarchical Cluster Analysis (HCA), Imperialist Competitive Algorithms (ICAs) and Support Vector Machines (SVMs) were used by cluster validity indices. The best algorithm for clustering was SVMs with three clusters, designated as leading delivery-based flexibility, frugal flexibility and sufficient plan-based flexibility.

S-Transform-Based Classification of Converter Faults in HVDC System by Support Vector Machines

2013 ◽  
Vol 347-350 ◽  
pp. 1308-1312
Author(s):  
Si Zhuo Lv ◽  
Jun Wen ◽  
Si Yu Zhou ◽  
Wen Jia Cao
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Classification Tree ◽  
Simulation Software ◽  
Good Time ◽  
Support Vector ◽  
Time Frequency ◽  
Hvdc System ◽  
S Transform ◽  
Vector Machines

Based on Support Vector Machines (SVM) and S-transform, a novel approach to detect and classify various types of high voltage direct current (HVDC) converter faults is presented. An electro-magnetic transient state simulation software PSCAD/EMTDC was used to set up a simulation model of HVDC system to investigate the typical converter faults. For the good time-frequency characteristic of S-transform, S-transform is applied to obtain useful features of the non-stationary fault signals. Then fault types are identified through the pattern recognition classifier based on SVM classification tree. Numerical results show that the proposed classification method is an effective technique for building up a pattern recognition system for converter fault signals.

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