Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (1): 20-25.doi: 10.6040/j.issn.1671-7554.0.2019.1009

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Value of blood routine data in distinguishing myelodysplastic syndrome and acute myeloid leukemia

DAI Xiaoyu1,2, LU Yuan1,2, WANG Zhiheng1,2, LI Mingzhuo1,2, SI Shucheng1,2, LI Jiqing1,2, JING Ming2, XUE Fuzhong1,2   

  1. 1. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    2. Institute for Medical Detaology, Shandong University, Jinan 250002, Shandong, China
  • Published:2022-09-27

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Abstract: Objective To establish a model to identify myelodysplastic syndrome(MDS)and acute myeloid leukemia(AML)based on blood routine data. Methods The data of 1 681 patients from Shandong Multi-Center Health Medical Big Data Platform were randomly divided into the training set(70%)and testing set(30%). MDS and AML were identified with random forest model. The discriminatory ability of the model was determined with the area under the receiver operating characteristic curve(AUC)and the stability of the model was tested with ten-fold cross validation. Results Both of the random forest model and support vector machine model were able to identify MDS and AML, but the former had better performance. It showed the estimated AUC for male was 0.874(95%CI: 0.815-0.932), sensitivity was 81.1%, and specificity was 81.9%; the estimated AUC for female was 0.831(95%CI: 0.752-0.911), sensitivity was 77.8%, and specificity was 74.3%. The ten-fold cross validation showed the estimated AUC was 0.884(95%CI: 0.854-0.913)for male and 0.842(95%CI: 0.802-0.883)for female. Conclusion The discriminant model is capable of identifying MDS and AML based on blood routine data.

Key words: Blood routine, Random forest, Myelodysplastic syndrome, Acute myeloid leukemia, ROC curve

CLC Number: 

  • R551.3
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