Unveiling Sociodemographic and Economic Drivers of Suicide Using Machine Learning: Toward Ethical and Effective Prevention Strategies

DOI

10.2991/978-94-6463-700-7_18How to use a DOI?

Keywords

Sociodemographic; Socioeconomics; Suicide Analysis; Machine Learning

Abstract

General national suicide is one of the major cover health issues which are influenced by various sociological and economic factors at large. Building on the ML approach, this work aims at analysing the key sociodemographic and socio-economic factors affecting the rates of suicide and the fundamental causal aspects. SUICIDE Among different factors of study, this research empirically explores cross-sectional relationship of age, gender, geographical distribution, and reasons for suicide including economic synthesized factors such as unemployment rates and economic downturn. Both ensemble and regression models are applied in order to analyze the data and identify patterns or correlations. The focus is especially vivid on the ethical use of artificial intelligence since crucial issues of fairness, accountability, and transparency of predictive models for sensitive societal questions are raised. Thus, the envisaged research proposes a set of empirically supported frameworks for policymakers to develop relevant and efficient interventions. In line with the principles of responsible AI, the findings show the possibility of machine learning in enabling datasets to guide public health interventions which are socially inclusive, ethologically right, and programmed to reduce on the multitude factors that cause suicide. Within this work, AI is presented as enabler of change for the betterment of society and support to sustainable mental health strategies.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Khushboo Rathore
AU  - Pradeep Kumar Mishra
AU  - Mritunjay K. Ranjan
AU  - Prasad Gadekar
AU  - Rahul Mandal
AU  - Kailas Doke
PY  - 2025
DA  - 2025/04/19
TI  - Unveiling Sociodemographic and Economic Drivers of Suicide Using Machine Learning: Toward Ethical and Effective Prevention Strategies
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 218
EP  - 240
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_18
DO  - 10.2991/978-94-6463-700-7_18
ID  - Rathore2025
ER  -