A REVIEW ON MACHINE LEARNING APPROACHES FOR THE DETECTION OF SUICIDAL TENDENCIES - Review and analysis

Aug 3, 2026

Upload by Asher

With the increasing prevalence of mental health issues, particularly suicidal behaviors, the need for early and accurate detection has become critical. This paper reviews machine learning approaches for detecting suicidal tendencies, surveying techniques across social media, clinical records, psychological assessments, self-reported instruments such as PHQ-9, audio speech recordings, and multimodal data. The review identifies dataset-specific nuances, key features, and reported results, while also detailing challenges and limitations. It aims to inform future research and improve early detection to help reduce suicide rates.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Suicidal Tendency

Warning Signs and Risk Factors

Scholarly Publisher RS Global Sp. z O.O. ISNI: 0000 0004 8495 2390 Dolna 17, Warsaw, Poland 00-773 Tel: +48 226 0 227 03 Email: [editorial_office@rsglobal.pl](editorial_office@rsglobal.pl) | JOURNAL | World Science | | --- | --- | | p-ISSN | 2413-1032 | | e-ISSN | 2414-6404 | | PUBLISHER | RS Global Sp. z O.O., Poland | | ARTICLE TITLE | A REVIEW ON MACHINE LEARNING APPROACHES FOR THE DETECTION OF SUICIDAL TENDENCIES | | --- | --- | | AUTHOR(S) | Kazi Golam Rabbany, Aisultan Shoiynbek, Darkhan Kuanyshbay, Assylbek Mukhametzhanov, Akbayan Bekarystankyzy, Temirlan Shoiynbek | | ARTICLE INFO | Kazi Golam Rabbany, Aisultan Shoiynbek, Darkhan Kuanyshbay, Assylbek Mukhametzhanov, Akbayan Bekarystankyzy, Temirlan Shoiynbek. (2024) A Review on Machine Learning Approaches for the Detection of Suicidal Tendencies. World Science. 3(85) . doi:
10.31435/rsglobal_ws/30092024/8222 | | DOI | [https://doi.org/10.31435/rsglobal_ws/30092024/8222](https://doi.org/10.31435/rsglobal_ws/30092024/8222) | | RECEIVED | 12 August 2024 | | ACCEPTED | 16 September 2024 | | PUBLISHED | 19 September 2024 | |
LICENSE This work is licensed under a Creative Commons Attribution
4.0 International License. | | © The author(s) 2024. This publication is an open access article. A REVIEW ON MACHINE LEARNING APPROACHES FOR THE DETECTION OF SUICIDAL TENDENCIES Kazi Golam Rabbany Narxoz University ORCID ID: 0009-0007-4549-0815 Aisultan Shoiynbek PhD, Professor, Narxoz University ORCID ID: 0000-0002-9328-8300 Darkhan Kuanyshbay PhD, Assistant Professor, SDU University ORCID ID: 0000-0001-5952-8609 Assylbek Mukhametzhanov Master’s student, SDU University ORCID ID: 0009-0009-8528-9985 Akbayan Bekarystankyzy PhD, Senior lecturer, Narxoz University ORCID ID: 0000-0003-3984-2718 Temirlan Shoiynbek Ms, Senior lecturer, Narxoz University DOI: [https://doi.org/10.31435/rsglobal_ws/30092024/8222](https://doi.org/10.31435/rsglobal_ws/30092024/8222) ARTICLE INFO Received: 12 August 2024 Accepted: 16 September 2024 Published: 19 September 2024 KEYWORDS Suicide Prevention, Depression Detection, Machine Learning, Natural Language Processing, Speech Analysis, Social Media Data, Clinical Data Analysis. ABSTRACT With the increasing prevalence of mental health issues, particularly suicidal behaviors, the need for early and accurate detection has become critical. This paper explores the current landscape of machine learning approaches used for the detection of suicidal tendencies. It examines a wide range of machine learning techniques applied to various data sources, including social media, clinical records, psychological assessments, self-reported forms like PHQ-9, audio speech recordings, and multimodal data integrating speech and visual information. This comprehensive review aims to reveal the types of existing research based on these varied datasets, highlighting the nuances of data collection, significant features identified, and the results obtained by different studies. Additionally, the review discusses the challenges and limitations associated with these approaches, providing researchers and practitioners with valuable insights into the potential and pitfalls of machine learning applications in diagnosing individuals at risk of suicide. The goal is to inform future research and improve early detection methods to ultimately reduce suicide rates. | Citation: Kazi Golam Rabbany, Aisultan Shoiynbek, Darkhan Kuanyshbay, Assylbek Mukhametzhanov, Akbayan Bekarystankyzy, Temirlan Shoiynbek. (2024) A Review on Machine Learning Approaches for the Detection of Suicidal Tendencies. World Science. 3(85). doi: 10.31435/rsglobal_ws/30092024/8222 | | --- | | Copyright: © 2024 Kazi Golam Rabbany, Aisultan Shoiynbek, Darkhan Kuanyshbay, Assylbek Mukhametzhanov, Akbayan Bekarystankyzy, Temirlan Shoiynbek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, di

FAQs

What is the main goal of this review on suicidal tendency detection?

The review aims to survey current machine learning approaches for detecting suicidal tendencies and clarify what types of research exist across different datasets, features, and results.

Which data sources are covered by the machine learning approaches discussed?

The paper covers social media, clinical records, psychological assessments, self-reported forms like PHQ-9, audio speech recordings, and multimodal data integrating speech and visual information.

What key challenges and limitations are emphasized?

The review discusses challenges and limitations tied to these approaches, focusing on difficulties that arise from data collection nuances and how models perform when diagnosing individuals at risk of suicide.

Aug 3, 2026

Upload by Asher