A Machine Learning Perspective on Causes of Suicides and identification of Vulnerable Categories using Multiple Algorithms

Shreemali, J.; Chakrabarti, P.; Chakrabarti, T.; Poddar, S.; Sipple, D.; Kateb, B.; Nami, M.

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.