Background: Suicides represent a social tragedy with long term impact for the family. Given the growing incidence of suicides, a better understanding of factors causing it and addressing them has emerged as a social imperative. Material and Methods: This study analyzed suicide data for three decades (1987-2016) and was carried out in two phases. Machine Learning Models run after pre-processing the suicide data included Neural network, Regression, Random Forest, XG Boost Tree, CHAID, Generalized Linear, Random Trees, Tree-AS and Auto Numeric Model. Results and Conclusion: Analysis of findings suggested that the key predictors for suicide are Age, Gender, and Country. In the second phase, data from happiness reports were merged with suicide data to check if Country-specific factors impact the list or order of key predictors. While the key predictors remain the same, Country-specific factors like Generosity, Health and Trust impact the suicide rate in the Country.

中文翻译：

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机器学习视角下的自杀原因和使用多种算法识别易受伤害的类别

背景：自杀代表了对家庭造成长期影响的社会悲剧。鉴于自杀事件的发生率不断上升，对导致自杀的因素进行更好的理解并解决这些问题已成为社会的当务之急。材料和方法：本研究分析了三个十年（1987-2016年）的自杀数据，并分两个阶段进行。在对自杀数据进行预处理之后运行的机器学习模型包括神经网络，回归，随机森林，XG Boost树，CHAID，广义线性，随机树，Tree-AS和自动数字模型。结果与结论：对调查结果的分析表明，自杀的主要预测因素是年龄，性别和国家。在第二阶段，将幸福报告中的数据与自杀数据合并，以检查特定国家/地区的因素是否影响关键预测变量的列表或顺序。