Graph-Based Community Detection in Hepatitis C Patient Data Using KNN Graph Construction and the Louvain Algorithm

Abstract

Analyzing medical data to identify clinically relevant patient groups remains a complex task, particularly for hepatitis C virus patients, as traditional clustering methods often struggle to capture heterogeneous patient relationships and may rely on specific geometric assumptions or be constrained by the number of groups to be found. The proposed framework for detecting patient communities combines local similarity representation and global structural analysis. This framework builds a network of patient similarity using the K-Nearest Neighbors (KNN) algorithm, followed by the detection of patient communities through the Louvain algorithm. The proposed method is implemented on two real hepatitis C virus (HCV) datasets: the Egyptian HCV dataset and the HCV (UCI) dataset. The results of the experiments show effective detection of coherent and stable patient communities. The highest modularity values reach 0.740 and 0.805 for the Egyptian and UCI datasets respectively, at a neighborhood value of K=3. In addition, in this context, low distortion values indicate strong cohesion within the detected communities. Overall, the results confirm that graph-based patient community discovery provides a powerful alternative to traditional clustering techniques for medical data analysis. The proposed framework enables reliable and stable discovery of clinically relevant and interpretable latent patient subgroups.