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Abstract

The Application of Decision Tree Model in Differential Diagnosis of Myelodysplastic Syndrome and Aplastic Anemia

Author(s): SONG. J*, WANG. Y, MEIRONG YANG1 AND RUI LIU
Chemoradiotherapy Department, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, 063000, P.R. China, Department of Hematology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, 063000, P.R. China

Correspondence Address:
Department of Hematology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, 063000, P.R. China, E-mail: [email protected]

The differential diagnosis models of C5.0, classification and regression trees and QUEST decision-making trees were established to provide a basis for the diagnosis of myelodysplastic syndrome and aplastic anemia. Patients with myelodysplastic syndrome and aplastic anemia hospitalized in hematology hospital of the Chinese Academy of Medical Sciences from January 2008 to December 2014 were selected as the study subjects. The general condition, clinical examination and laboratory examination data of the patients were collected using a self-designed questionnaire. The differential diagnosis models of C5.0, classification and regression trees and QUEST decision-making trees were established with 2 blood diseases as dependent variables and their differential diagnosis indices as independent variables. The performance of the 3 models was compared using the indices of accuracy, sensitivity and specificity. The prediction accuracy, sensitivity, specificity, F1 and Youden’s index of C5.0 decision-making tree model were 78.12, 87.50, 66.67, 81.48 and 0.54 %, respectively. The prediction accuracy, sensitivity, specificity, F1 and Youden’s index of classification and regression trees decision-making tree model were 73.75, 76.14, 70.83, 81.03 and 0.51 %, respectively. The prediction accuracy, sensitivity, specificity, F1 and Youden’s index of QUEST decision-making tree model were 76.88, 89.77, 61.11, 76.14 and 0.47 %, respectively. There was no statistical difference in the accuracy, specificity, F1 and Youden’s index of the 3 models. The specificity of C5.0 decision-making tree and QUEST decision-making tree models were significantly higher than classification and regression trees decision-making tree model (p<0.05). The C5.0 decision-making tree model has higher prediction accuracy, sensitivity, specificity, F1, and Youden’s index, which were superior to the other two models and can be used as auxiliary models for differential diagnosis of myelodysplastic syndrome and aplastic anemia.

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