Data mining Application for Exploring the Relation-ship between Addiction and Depression

In recent years, increasing the number of people with addiction and depression has become one of the major challenges in our country. This has a very important role in increasing the number of deaths, suicides, and murders caused by drug use in the society, in addition to the high economic burden due to the rehabilitation and treatment costs, which are imposed to the economic system of the community. Therefore, creating a method or methods for identifying those at risk of developing any of the listed diseases (addiction or depression) and considering preventive measures is one of the important requirements, which prevents the waste of medical expenses and financial or life loss damages in the community . In this paper, the data related to the patients including the results of clinical and para-clinical tests, personal information (such as gender, age, education level, place of residence, economic status, etc.) were collected. Then, data mining techniques such as clustering, discovering forum relationships between various variables (properties) were applied on these data. Finally, a model will be presented to any of the diseases (addiction or depression) with a high degree of reliability and accuracy to help clinicians to identify individuals at high risk. In addition, the relationship between the two diseases can be identified by creating forum rules. This model can be used as a medical assistant or worker in remote or disadvantaged areas in the case of implementing in the form of a smart system.

Addiction, Depression, Data mining, Clustering, Medical Assistant, Smart System