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New method for assessing suicide ideation based on an attention mechanism and spiking neural network

The COVID-19 pandemic has had a substantial effect on global mental health, leading to increased depression and suicide ideation (SI), particularly among young adults. This study introduces a novel method for enhancing SI assessment in young adults with depression, utilizing machine learning (ML) te...

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Main Authors: Corrine, Francis, Abdulrazak Yahya, Saleh
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU). 2025
Subjects:
Q Science (General)
Online Access:http://ir.unimas.my/id/eprint/46777/1/25208-55776-1-PB.pdf
http://ir.unimas.my/id/eprint/46777/
https://ijai.iaescore.com/index.php/IJAI/article/view/25208
http://doi.org/10.11591/ijai.v14.i1.pp350-357
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spelling my.unimas.ir-467772024-12-02T06:11:07Z http://ir.unimas.my/id/eprint/46777/ New method for assessing suicide ideation based on an attention mechanism and spiking neural network Corrine, Francis Abdulrazak Yahya, Saleh Q Science (General) The COVID-19 pandemic has had a substantial effect on global mental health, leading to increased depression and suicide ideation (SI), particularly among young adults. This study introduces a novel method for enhancing SI assessment in young adults with depression, utilizing machine learning (ML) techniques applied to structural magnetic resonance imaging (SMRI) data. SMRI data from 20 individuals with depression and 60 healthy controls were analyzed. A hybrid ML algorithm, integrating self-attention mechanism and evolving spiking neural networks, successfully classified depression with 94% accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These results offer potential for enhancing mental health intervention and support in the context of the ongoing and post-pandemic period influenced by COVID-19. Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU). 2025-02 Article PeerReviewed text en http://ir.unimas.my/id/eprint/46777/1/25208-55776-1-PB.pdf Corrine, Francis and Abdulrazak Yahya, Saleh (2025) New method for assessing suicide ideation based on an attention mechanism and spiking neural network. IAES International Journal of Artificial Intelligence (IJ-AI), 14 (1). pp. 350-357. ISSN 2252-8938 https://ijai.iaescore.com/index.php/IJAI/article/view/25208 http://doi.org/10.11591/ijai.v14.i1.pp350-357
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Corrine, Francis
Abdulrazak Yahya, Saleh
New method for assessing suicide ideation based on an attention mechanism and spiking neural network
description The COVID-19 pandemic has had a substantial effect on global mental health, leading to increased depression and suicide ideation (SI), particularly among young adults. This study introduces a novel method for enhancing SI assessment in young adults with depression, utilizing machine learning (ML) techniques applied to structural magnetic resonance imaging (SMRI) data. SMRI data from 20 individuals with depression and 60 healthy controls were analyzed. A hybrid ML algorithm, integrating self-attention mechanism and evolving spiking neural networks, successfully classified depression with 94% accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These results offer potential for enhancing mental health intervention and support in the context of the ongoing and post-pandemic period influenced by COVID-19.
format Article
author Corrine, Francis
Abdulrazak Yahya, Saleh
author_facet Corrine, Francis
Abdulrazak Yahya, Saleh
author_sort Corrine, Francis
title New method for assessing suicide ideation based on an attention mechanism and spiking neural network
title_short New method for assessing suicide ideation based on an attention mechanism and spiking neural network
title_full New method for assessing suicide ideation based on an attention mechanism and spiking neural network
title_fullStr New method for assessing suicide ideation based on an attention mechanism and spiking neural network
title_full_unstemmed New method for assessing suicide ideation based on an attention mechanism and spiking neural network
title_sort new method for assessing suicide ideation based on an attention mechanism and spiking neural network
publisher Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).
publishDate 2025
url http://ir.unimas.my/id/eprint/46777/1/25208-55776-1-PB.pdf
http://ir.unimas.my/id/eprint/46777/
https://ijai.iaescore.com/index.php/IJAI/article/view/25208
http://doi.org/10.11591/ijai.v14.i1.pp350-357
_version_ 1817848779779342336
score 13.244413

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