Analysis of Disease Data Based on Neo4j Graph Database
Jian Zhao, Zhiguo Hong, Minyong Shi · 2019
As we all know, there are many diseases in the natural environment in which we humans live. A disease may show a variety of symptoms in patients, such as appendicitis, a variety of symptoms of appendicitis including abdominal pain, fever, gastrointestinal reactions and so on. At the same time, a symptom may also correspond to a variety of diseases. The relational database uses a large number of links to represent and query these complex and larger correspondences, which is very expensive. Many data relationships in the real world are graphical, and the graph database can better describe such data [1]. The Neo4j graph database's data store has pointers to their neighbors, so it's easy to extend the newly discovered content. In addition, querying highly correlated data is very efficient for the Neo4j graph database. In the graphical database, the symptoms associated with each disease and the association between the disease and the disease can be clearly displayed to help people better judge the disease. This paper first introduces the data into the Neo4j graph database, and then introduces Neo4j's query language Cypher, so as to make a more intuitive image analysis and provide corresponding treatment suggestions for the disease data of related queries.