Suicide Rate Analysis Using Extra Trees Algorithm

8 pages•Published: August 6, 2024

Ignisha Rajathi G, Sneharajalakshmi Palanisamy, Midhun Kumar V N and Vikash Kumar S

Abstract

In recent times, suicide has emerged as a significant global public health concern, causing profound harm to individuals, families, and communities. Despite intensified efforts to address mental health challenges, suicide rates continue to surge in numerous regions worldwide. To effectively tackle this escalating problem, there is an urgent requirement for a thorough examination and understanding of the factors driving suicide occurrences. The primary objective is to conduct a comprehensive analysis of suicide rates utilizing advanced analytics techniques. This analysis endeavors to uncover the intricate network of socio-economic, demographic, and psychological factors shaping suicide trends across various countries and regions. Through meticulous investigation and interpretation of these factors, this research aims to gain insights that can inform targeted interventions and policies aimed at mitigating the prevalence of suicide and promoting mental well-being on a global scale.

Keyphrases: extra trees regressor, mental health, predictive model, suicide prevention, suicide rate analysis

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 53-60.

Links:	https://easychair.org/publications/paper/BJdn
	https://doi.org/10.29007/4xxg

BibTeX entry

@inproceedings{ICSSIT2024:Suicide_Rate_Analysis_Using,
  author    = {Ignisha Rajathi G and Sneharajalakshmi Palanisamy and Midhun Kumar V N and Vikash Kumar S},
  title     = {Suicide Rate Analysis Using Extra Trees Algorithm},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/BJdn},
  doi       = {10.29007/4xxg},
  pages     = {53-60},
  year      = {2024}}

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