Clustering-based visualizations for diagnosing diseases on metagenomic data

Metagenomic data has recently become crucial for precision or personalized medicine. However, these data are often complex, challenging to observe and require sophisticated visualization approaches such as clustering algorithms. Additionally, leveraging the robustness of a simple deep learning architecture, such as a shallow convolutional neural network, has attracted many scientists. Therefore, this study utilized well-known clustering algorithms such as density-based spatial clustering of applications with noise (DBSCAN), balanced iterative reducing and clustering using hierarchies (BIRCH), and ordering points to identify the clustering structure (OPTICS) to identify patterns in complex data and generate visualizations from species abundance composition of various diseases. The study then integrated a shallow convolutional neural network to perform disease prediction tasks on clustering-based visualizations. Experimental results showed that BIRCH outperformed some studies in diagnosing Type 2 diabetes, while DBSCAN performed well in diagnosing Colorectal cancer and Inflammatory Bowel Disease.