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. 2019 Aug 15;7(3):e13476. doi: 10.2196/13476

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©Jiangpeng Wu, Xiangyi Zan, Liping Gao, Jianhong Zhao, Jing Fan, Hengxue Shi, Yixin Wan, E Yu, Shuyan Li, Xiaodong Xie. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 15.08.2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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The detailed forest structure for the RBLC model. (A) The general structure of the voting strategy of the RBLC model. (B) The independent decision rulings for different blood indices for the first tree (T1) in (A). T: tree; WBC: white blood cell count; NE%: neutrophil granulocyte ratio; LY%: lymphocyte ratio; MO%: monocyte ratio; BA%: basophil ratio; NE#: neutrophilic granulocytes; MO#: monocytes; EO#: eosinophil cells; MCV: mean corpuscular volume; MCH: mean corpuscular hemoglobin; PDW: platelet distribution width; P-LCR: platelet large cell ratio; UREA: carbamide; ALP: alkaline phosphatase; ALB: albumin; A/G: albumin/globulin; CK: creatine kinase; CK-MB: creatine kinase isoenzymes; LDH: lactate dehydrogenase.