Unsupervised Analysis of Clinical and Laboratory Parameters of Chronic Kidney Disease

Chronic Kidney Disease (CKD) results from kidney injuries and leads to a decline in renal function. This study used unsupervised machine learning techniques, focusing on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method. After applying DBSCAN to clinical and laboratory data from patients with or at risk of CKD, we employed Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize the findings. The findings underscore the complexity of analyzing health data. However, the DBSCAN method effectively categorized patients into distinct groups based on the severity of their chronic kidney disease (CKD), allowing for a more precise differentiation of patients according to their risk levels associated with different stages of the disease.