Application of BP neural network in renal pathological prediction in children with hematuria as main clinical manifestation
10.3760/cma.j.cn101070-20190510-00395
- VernacularTitle:BP神经网络在以血尿为主要表现患儿肾脏病理预测中的应用
- Author:
Junli WAN
1
;
Jiacheng LI
;
Gaofu ZHANG
;
Mo WANG
Author Information
1. 重庆医科大学附属儿童医院肾脏内科，儿童发育疾病研究教育部重点实验室，儿童发育重大疾病国家国际科技合作基地，儿童感染免疫重庆市重点实验室 400014
- From:
Chinese Journal of Applied Clinical Pediatrics
2020;35(5):365-369
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the differences in clinical indicators of different pathological types of children with hematuria as the main manifestation, and to establish a BP neural network prediction model based on clinical data.Methods:The clinical data and renal pathological results of children who were referred to Children′s Hospital of Chongqing Medical University from June 2003 to December 2018 for evaluation of hematuria as the main manifestation were collected, the significant differences in these clinical indicators were analyzed, and a BP neural network model for predicting renal pathology in children with hematuria as the main manifestation was established.Results:A total of 438 cases were enrolled in this study, including 232 males and 206 females, with the onset age of (7.00±3.15) years old.According to different clinical manifestations, the children were divided into microscopic hematuria group(179 cases), gross hematuria group(81 cases), microscopic hematuria and proteinuria group (44 cases), and gross hematuria and proteinuria group(134 cases). There were significant differences in sex ratio, onset age, course of disease, inducement, Addis count of urinary red cells, 24-hour proteinuria, blood urea nitrogen, serum creatinine, serum albumin and serum IgA levels among different clinical manifestations (all P< 0.05). Pathological grouping indicated that there were significant differences in sex ratio, onset age, course of disease, family history, Addis count of urinary red cells, 24-hour proteinuria, blood urea nitrogen, serum creatinine, serum albumin, serum IgA and C 3 levels among different pathological groups (all P< 0.05). The BP neural network prediction model was then constructed based on the above indicators, and the accuracy of the prediction model was measured to be 61.19% by using the leave one out method. Conclusions:By comparing the differences of various indicators under different clinical manifestations and pathological types, a BP neural network prediction model for renal pathology in children with hematuria as the main manifestation is established.The model can accurately predict renal pathology with the help of related indicators, and provides a basis for determining the time of kidney biopsy.
