Healthcare data records using neo4j graph database model

Abstract

Graph databases are based on graph theory, just as SQL and RDBMS are based on set theory and logic. The relationships that Graph databases model are not data. Relational database management systems can now be replaced with graph databases. Applications that can be represented in a much more natural form include those in chemistry, biology, the semantic web, social networking, and recommendation engines. Relational database management systems (Oracle, MySQL) and graph databases will be compared, with an emphasis on elements such as data structures, data model features, and query capabilities. The inherent and modern limits of some of the existing comparisons and contrasts between implementations of graph and relational databases will also be discussed. Graph problems look for the lowest collection of nodes that cover a graph, the shortest path among a set of paths in a transportation network, and so forth. Although there is no ANSI/ISO standard language for graphs, the most well-known ones include Neo4j, Orient DB, and Amazon Neptune. They frequently base their syntax on SQL or math. In this paper, a healthcare database using NodeJS and connected to neo4j community edition as a type of graph database, with a maximum of three lakh datasets containing information about people, doctors, patients,histories, allergy histories, complaints, treatment episodes, and visits with their IDs, details, and other attributes. Utilizing NodeJS and the data saved in the provided Excel sheet, the data counts and data sizes for each graph are computed at various intervals of the dataset. CQL is utilized in this case to analyze how long various queries take to execute. A healthcare database using NodeJS and connected to neo4j community edition as a type of graph database, with a maximum of three lakh datasets containing information about people, doctors, patients, histories, allergy histories, complaints, treatment episodes, and visits with their IDs, details, and other attributes. Utilizing NodeJS and the data saved in the provided Excel sheet, the data counts and data sizes for each graph are computed at various intervals of the dataset. CQL is utilized in this case to analyze how long various queries take to execute.