Risk factor mining and prediction of urine protein progression in chronic kidney disease: a machine learning- based study

Abstract

Background: Chronic kidney disease (CKD) is a global public health concern. Therefore, to provide timely intervention for non-hospitalized high-risk patients and rationally allocate limited clinical resources is important to mine the key factors when designing a CKD prediction model.

Methods: This study included data from 1,358 patients with CKD pathologically confirmed during the period from December 2017 to September 2020 at Zhongshan Hospital. A CKD prediction interpretation framework based on machine learning was proposed. From among 100 variables, 17 were selected for the model construction through a recursive feature elimination with logistic regression feature screening. Several machine learning classifiers, including extreme gradient boosting, gaussian-based naive bayes, a neural network, ridge regression, and linear model logistic regression (LR), were trained, and an ensemble model was developed to predict 24-hour urine protein. The detailed relationship between the risk of CKD progression and these predictors was determined using a global interpretation. A patient-specific analysis was conducted using a local interpretation.

Results: The results showed that LR achieved the best performance, with an area under the curve (AUC) of 0.850 in a single machine learning model. The ensemble model constructed using the voting integration method further improved the AUC to 0.856. The major predictors of moderate-to-severe severity included lower levels of 25-OH-vitamin, albumin, transferrin in males, and higher levels of cystatin C.

Conclusions: Compared with the clinical single kidney function evaluation indicators (eGFR, Scr), the machine learning model proposed in this study improved the prediction accuracy of CKD progression by 17.6% and 24.6%, respectively, and the AUC was improved by 0.250 and 0.236, respectively. Our framework can achieve a good predictive interpretation and provide effective clinical decision support.

Keywords: Chronic kidney disease; Clinical decision support; Machine learning; Model interpretation.

Fig. 1

Chronic kidney disease (CKD) prediction and decision support framework. A total of 1,358 patients were included in this study, with 100 clinical variables applied. The data were divided into training (80%) and validation (20%) sets. The model was trained using k-fold cross-validation (k = 10), and a grid search was conducted to determine the best parameter combinations

Fig. 2

Screening of predictors and evaluation of models. (a) RFE-LR used to examine whether any subset of the input features can achieve a better discrimination than the initial set of features. (b) ROC curves of different models on the validation sets. (c) Precision–recall (PR) curves of different models on the validation sets

Fig. 3

SHAP summary plot of the top-17 features of the ensemble model. The abscissa is the SHAP value, which represents the impact on the model output. The ordinates are different features, with red representing larger eigenvalues, and blue indicating smaller eigenvalues

Fig. 4

SHAP dependence plots for ensemble model. The SHAP-dependence plot shows the effect of a single feature on the output of the ensemble prediction model. SHAP values for specific features exceeding zero represent an increased risk of CKD progression. (a-f) 25-hydroxyvitamin D, albumin, cystatin C, glycated albumin, estimated glomerular filtration rate (eGFR), transferrin, protein A1, uric acid, and total protein

Fig. 5

Overall summary of the study. Using common clinical variables, machine learning based approaches can effectively predict and explain the progression of CKD. Furthermore, decision support is provided for early intervention, and medical resource allocation is given for outpatients and those requiring a follow-up