Song Wenzhu, Zhou Xiaoshuang, Duan Qi, Wang Qian, Li Yaheng, Li Aizhong, Zhou Wenjing, Sun Lin, Qiu Lixia, Li Rongshan, Li Yafeng
School of Public Health, Shanxi Medical University, Taiyuan, China.
Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.
Front Med (Lausanne). 2022 Jul 28;9:911737. doi: 10.3389/fmed.2022.911737. eCollection 2022.
Chronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD.
Demographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively.
A total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively.
RF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice.
慢性肾脏病（CKD）是一种常见的慢性疾病，发病率高且起病隐匿。肾小球损伤（GI）和肾小管损伤（TI）是CKD的早期表现，可提示其发展风险。在本研究中，我们旨在使用三种机器学习算法对GI和TI进行分类，以促进其早期诊断并减缓CKD的进展。
首先从山西省10个县的13550名受试者中收集人口统计学信息、体格检查、血液和晨尿样本，用于GI和TI的分类。此外，采用LASSO回归进行解释变量的特征选择，并使用SMOTE（合成少数过采样技术）算法平衡目标数据集，即GI和TI。随后，构建随机森林（RF）、朴素贝叶斯（NB）和逻辑回归（LR）分别实现GI和TI的分类。
本研究共纳入12330名参与者，有20个解释变量。GI患者和TI患者的数量分别为1587例（12.8%）和1456例（11.8%）。经LASSO特征选择后，这两个数据集中分别保留了14个和15个解释变量。此外，经SMOTE处理后，GI组患者和正常者的数量分别为6165例、6165例，TI组分别为6165例、6164例。RF在GI和TI的准确率（78.14%、80.49%）、灵敏度（82.00%、84.60%）、特异度（74.29%、76.09%）和AUC（0.868、0.885）方面均优于NB和LR；对GI和TI分类贡献最大的四个变量分别为收缩压、舒张压、性别、年龄以及年龄、收缩压、空腹血糖和糖化血红蛋白。
RF在GI和TI的分类中表现良好，可实现GI和TI的早期辅助诊断，从而有助于减缓CKD的进展，在临床实践中具有广阔前景。
