Suicidal Ideation Detection Using Machine Learning

Naga Prabhakar Ejaru, Ramya Sree Gangana, Karuna Kuruba, Pallavi Uppara, Pavan Sai Chinduluru

2025

Abstract

Recognizing self-destructive ideation is a basic area of examination that expects to distinguish people who might be in danger of self-damage or self-destruction. Early ID is fundamental for further developing intercession and treatment results, eventually diminishing the probability of deadly outcomes. This exploration centers around making a successful structure for perceiving self-destructive considerations utilizing different strategies, like social and mental examination. By following key markers like changes in correspondence, profound prosperity, and social separation, this system plans to anticipate self-destructive ideation and empower convenient mediations. The review inspects the utilization of clinical assessments, mental polls, and example acknowledgment strategies to recognize people in danger. Moreover, it investigates how medical services experts can coordinate these devices into their clinical practices to upgrade the precision of appraisals and intercessions. The general objective is to create a harmless, dependable, and open location framework that can be utilized in different conditions, including clinics, emotional wellness places, and local area-based drives. This preventive methodology plans to lessen the disgrace related with looking for help for self-destructive considerations and to raise worldwide mindfulness about emotional wellness challenges.

Paper Citation

in Harvard Style

Ejaru N., Gangana R., Kuruba K., Uppara P. and Chinduluru P. (2025). Suicidal Ideation Detection Using Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 93-99. DOI: 10.5220/0013892300004919

in Bibtex Style

@conference{icrdicct`2525,
author={Naga Ejaru and Ramya Gangana and Karuna Kuruba and Pallavi Uppara and Pavan Chinduluru},
title={Suicidal Ideation Detection Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={93-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013892300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Suicidal Ideation Detection Using Machine Learning
SN - 978-989-758-777-1
AU - Ejaru N.
AU - Gangana R.
AU - Kuruba K.
AU - Uppara P.
AU - Chinduluru P.
PY - 2025
SP - 93
EP - 99
DO - 10.5220/0013892300004919
PB - SciTePress