Early and accurate prediction of the risk of developing end-stage renal disease (ESRD) is essential for medical decision-making. In the field of chronic kidney disease (CKD), previous studies have reported the impact of various factors and the percentage decline in estimated glomerular filtration rate (eGFR) in the previous 2 years on the development of ESRD from a medical perspective. Traditional risk assessment methods usually rely on expert experience, simple statistical analyses, and limited biomarkers, which face obvious limitations when dealing with complex, multidimensional health data, whereas the use of machine learning algorithms, such as artificial neural networks, can significantly improve the accuracy, sensitivity, and specificity of risk prediction.

Based on multiple algorithms, this study explored the predictive value of 2-year mean levels of clinical parameters and the rate of change of eGFR over a period of 2 years in the progression of CKD to ESRD.

The dataset for this study was obtained from a retrospective cohort of the Japanese CKD population at Teikyo University Hospital, Japan, from 2008 to 2014, 700 patients were enrolled in the study cohort. Two datasets were obtained based on this cohort, a baseline dataset and a 2-year time-averaged dataset. Logistic regression (LR), multilayer perceptual machine (MLP), support vector machine (SVM), extreme gradient boosting tree (XGBoost), and two-dimensional convolutional neural network (CNN) algorithms were used to predict whether a patient would reach ESRD after several years and to derive probabilities. The dataset is balanced at both the data and algorithmic levels, and medical significance is demonstrated using comparative trials.

Using LR, MLP, SVM, and XGBoost as the baseline models, the comparison experiments showed that the CNN model was the best, with an accuracy of 94.8%, precision of 80.3%, recall of 78.2%, and F1 score of 78.4%. The evaluation metrics of the five models on the two-year time-averaged dataset were significantly higher than those on the baseline dataset, especially the recall rate. In addition, models that included the eGFR decline rate variable over two years outperformed models that did not include this variable. Recall improved considerably after addressing the imbalance in the dataset categories.

This study demonstrates that a two-dimensional CNN model based on the CKD dataset can guide healthcare professionals to make better clinical treatment decisions, that the mean level of clinical parameters in the first 2 years and the percentage decline in eGFR over 2 years are significant in predicting dialysis events, and that comprehensive management in the first 2 years is essential to delay the onset of ESRD.