Chapter Ten - An application of cypher query-based dynamic rule-based decision tree over suicide statistics dataset with Neo4j

Abstract

Recent mental health statistical studies have proclaimed the increase in mortality rate due to a rise in suicides. The growth in suicide rates around the world seeks the attention of different health organizations to act toward the betterment of mental health of the people. Anatomizing the estimated suicide rate data provides insight on suicide prevention methods. This chapter examines the suicide rate dataset from the Global Health Observatory Repository by the World Health Organization. Analysis of the data is carried out by creating rule-based decision trees from a set of decision rules over the indexed data model represented in a tree template. The study is performed on the suicide rate data taking WHO region and country as standards. Neo4j, the popular graph database, is used in the process to model the data and to explore the data by traversing through the nodes to generate rule-based decision trees. The study also focuses on how indexing plays a vital role in creating rule-based decision trees by managing the response time.