Analysis of Suicide Risk in European Countries Using Artificial Intelligence Methods

Abstract

Suicides are a phenomenon that negatively impacts society. The issues include the loss of human life, negative psychological effects on close ones, increased healthcare costs, lost productivity, and more. Suicides also create social and emotional tensions that can deteriorate the quality of life in affected communities. This is why it is necessary to study and research trends to enable prevention.

In the present study, an analysis of suicide risk in some European countries is conducted using an artificial intelligence system implemented with Neural Networks, Random Forests, Support Vector Machines, and more. The study seeks answers to the following questions: Are suicide rates increasing or decreasing? Are there differences in the number of suicides based on age and gender? Do economic factors affect the number and rate of suicides? Can we predict the number of suicides based on the given data? The machine learning methods used for this purpose are created and modeled in the Orange Data Mining environment, which also offers numerous tools for analyzing and visualizing the obtained results. It is important to note that the study of suicide risk is not equivalent to the diagnosis of suicidal disorders; however, the answers to the questions posed in the study are crucial for building and analyzing the overall picture of suicidal intentions.