Differential Diagnosis Models for Multiple Myeloma Combined with Renal Injury and Chronic Kidney Disease or Nephrotic Syndrome

Mei-Na Wu1, Yong-Li Yang1, Jie-Bing Tan2, Xiao-Can Jia1, Jun-Zhe Bao1, Yu-Ping Wang1, Chao-Jun Yang1, Xue-Zhong Shi1, *
1 Department of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, China
2 Department of Public Health, Henan Provincial Center for Disease Control and Prevention, Zhengzhou, Henan 450001, China



When multiple myeloma(MM) is combined with renal injury, most patients are easily misdiagnosed as kidney diseases. This study aimed to establish a differential diagnosis model for MM combined with renal injury based on clinical information.


A total of 77 patients with MM combined with renal injury were recruited as the case group, and 112 patients with kidney diseases were recruited as a control group. Support vector machine (SVM), decision tree (DT), and artificial neural network (ANN) models were developed based on significant clinical variables. Accuracy and area under the receiver operating characteristic curve (AUC) were used to evaluate each model.


Accuracies of SVM, DT, and ANN were 0.843,0.902, and 0.941. The AUCs of SVM, DT, and ANN were 0.822,0.879, and 0.932. Lower extremity edema, bone pain, and lactate dehydrogenase (LDH) were common important indicators identified by SVM, DT and ANN models. When these three indicators were excluded, the ANN model prediction effect decreased significantly (P<0.05).


The results suggest that the ANN model best predicts the differential diagnosis between MM combined with renal injury and chronic kidney disease/nephrotic syndrome. Important features contributing to identifying the diseases, including lower extremity edema, bone pain, and LDH, may assist in diagnosing such diseases in the future.

Keywords: Multiple myeloma, renal injury, Chronic kidney disease, Nephrotic syndrome, Support vector machine, Decision tree, Artificial neural network.

Abstract Information

Identifiers and Pagination:

Year: 2023
Volume: 10
DOI: 10.2174/18742203-v10-e230419-2022-47

Article History:

Electronic publication date: 19/04/2023
Collection year: 2023

© 2023 Wu et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Zhengzhou University, Zhengzhou, Henan 450001, China; E-mail: xzshi@zzu.edu.cn