Journal article Open Access
Avhishek Biswas; Ananya Talukder; Deep Bhattacharjee; Arijit Chowdhury; Judhajit Sanyal
Many factors have led to the increase of suicide-proneness in the present era. As a consequence, many novel methods have been proposed in recent times for prediction of the probability of suicides, using different metrics. The current work reviews a number of models and techniques proposed recently, and offers a novel Bayesian machine learning (ML) model for prediction of suicides, involving classification of the data into separate categories. The proposed model is contrasted against similar computationally-inexpensive techniques such as spline regression. The model is found to generate appreciably accurate results for the dataset considered in this work. The application of Bayesian estimation allows the prediction of causation to a greater degree than the standard spline regression models, which is reflected by the comparatively low root mean square error (RMSE) for all estimates obtained by the proposed model.