机器学习在CKD基因筛查中的应用

We examined the GSE60861 dataset and found that it consists of two subsets with different data sizes, namely GSE45980 and GSE60860. In GSE45980 (N=43), we observed that the minimal z-score has moderate diagnostic capability for CKD progression (AUC: 0.758). In the future, we can use various machine learning algorithms, including SHAP, to screen for risk genes that make significant contributions.