Machine Learning Based Prediction of Suicide Probability
Avhishek Biswas1, Ananya Talukder2, Deep Bhattacharjee3, Arijit Chowdhury4, Judhajit Sanyal5

1Avhishek Biswas, Computer Science and Electrical Engineering, University of North Dakota, Grand Forks, North Dakota, United States of America.
2Ananya Talukder, Electronics and Communication Engineering, MAKAUT, Kolkata, India.
3Deep Bhattacharjee, Electronics and Communication Engineering, MAKAUT, Kolkata, India.
4Arijit Chowdhury, Electronics and Communication Engineering, MAKAUT, Kolkata, India.
5Judhajit Sanyal*, Electronics and Communication Engineering, MAKAUT, Kolkata, India. 

Manuscript received on September 02, 2020. | Revised Manuscript received on September 15, 2020. | Manuscript published on October 30, 2020. | PP: 94-97 | Volume-10 Issue-1, October 2020. | Retrieval Number: 100.1/ijeat.A17011010120 | DOI: 10.35940/ijeat.A1701.1010120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Bayesian model, classification, machine learning, spline regression, suicide prediction.