Identifying the links between symptoms and diseases is crucial for diagnosis and treatment, as they affect understanding of the disease and the development of medication. Through network analysis, we can examine these connections in detail by calculating different measures for them and identifying potential patterns. There are several ways to build a network of symptoms and diseases, for example, by linking them with the number of co-occurrences in abstracts of scientific articles. In the thesis, we build a network of symptoms and diseases by using the number of Google Search hits as the edge weight for each combination of symptom and disease. We focus on the network’s projection on diseases based on common symptoms and use different algorithms to find communities of diseases. The results obtained are analyzed, interpreted and compared with the results of a reference study.