Analyzing Suicide and Contributing Factors in Thailand Using Classification by Logistic Regression, Decision Tree and Support Vector Machine

The World Health Organization (WHO) reports that over 700,000 people die from suicide each year, with the number of attempts far exceeding that. Suicide is also the fourth leading cause of death among teenagers [1]. The suicide rate in Thailand follows a similar trend. This research aims to utilize machine learning techniques to identify cases that lead to suicide deaths and to study the contributing factors, both internal and external. Specifically, this paper proposes using classification methods to create a prediction model from people’s demography, behaviors, and other factors in order to analyze features that influence the risk of suicide deaths. Classification techniques employed include Logistic Regression, Decision Tree, and Support Vector Machine. We have analyzed the data gathered by Thailand National Suicide Prevention Department from January 2018 to September 2021, which includes 49,417 suicidal cases, 30% of which resulted in fatalities. Two models are proposed: a general model to identify significant factors leading to suicide deaths and a practical model to predict suicide deaths given only available factors in practice.