The visual representation of data analysis results, a current trend in data science, is essential for people without deep knowledge of statistics and machine learning. Patient Similarity Networks (PSNs), as an instance of similarity networks in general, are commonly used visualization tools in biomedical data analysis. PSNs are an understandable presentation of the complex relationships hidden in patient data, such as partitioning patients with similar characteristics into clusters, providing relatively easy interpretation to clinicians and clinical biologists. However, the interpretation may be inaccurate due to an incorrect intuition, especially in complex multi-class, multi-cluster situations. Our paper focuses on analyzing cluster-class relationships in PSNs, assuming different numbers of classes and clusters, both of different and potentially imbalanced sizes. We use several problematic situations to show how to support clinical intuition in interpreting these situations correctly. We use the Matthews correlation coefficient to analyze cluster-class relationships in a way that understandably complements the PSN visualization. Finally, we present a visualization of this approach on a real-world patient similarity network.