Suicide and Suicide Attempt Descriptors by Multimethod Approach

Abstract

Background: Suicide is a complex action of suicidal methods and peripheral factors with seemingly threatening components representing actual cause for the suicidal actions. It is especially those, apparently unimportant factors that represent a crucial milestone in the network of all the other, personal, cultural, genetic and biochemical factors, forming the method of action consequently deciding between life and death.

Subjects and methods: Based on the Register of Suicides in the Republic of Slovenia kept by the University Psychiatric Clinic Ljubljana, we used a combination of attributes varying within a variable and between variables. Due to limited application of standard statistical methods and analyses in such cases, we used the Machine learning method, Multimethod hybrid approach, which allows combining of different approaches to machine learning (decision trees, genetic algorithms and supplementary vectors). The research included 56712 persons attempting suicide and 21913 persons committing suicide. We chose a form of a suicide action with both possible results: attempted suicide and suicide.

Results: Based on the analysis of machine learning, we defined attributes of the action regarding their lethal effect: attempted suicide and suicide commitment. The suicide register kept for the last 40 years shows hanging as the most commonly used suicidal method, used by men with the purpose of causing suicidal death rather than a suicidal attempt. On the other hand, use of medicaments is linked to the suicidal attempt and mostly used by females.

Conclusions: All methods of suicidal actions cannot predict suicidal death, thus we examined different methods of suicide to most accurately predict the link between the method and its effect in terms of suicide attempt or suicide. The Machine learning method confirmed the attributes of suicide methods in connection with their different outcomes. This analytical method is useful in processing large databases since it enables one variable's intensity to affect other variables in terms of result and meaning. The identification of the most decisive risk factors for suicidal behaviour can serve as basis for planning an effective prevention strategies, timely identification and adequate proffessional help to the high risk persons.