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Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment

The coronavirus disease 2019 (COVID-19) pandemic has had a substantial detrimental impact on mental health, especially depression, and this has led to a high incidence of suicidal ideation (SI) around the globe, with the pandemic's post-peak period seeing the highest incidence in young adults....

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Main Authors: Corrine, Francis, Abdulrazak Yahya, Saleh
Format: Proceeding
Language:English
Published: 2023
Subjects:
QA76 Computer software
Online Access:http://ir.unimas.my/id/eprint/44290/1/Performance_Evaluation_of_Attention_Mechanism_and_Spiking_Neural_Networks_on_sMRI_Data_for_Suicide_Ideation_Assessment.pdf
http://ir.unimas.my/id/eprint/44290/
https://ieeexplore.ieee.org/document/10397625
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spelling my.unimas.ir.442902024-01-26T00:32:43Z http://ir.unimas.my/id/eprint/44290/ Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment Corrine, Francis Abdulrazak Yahya, Saleh QA76 Computer software The coronavirus disease 2019 (COVID-19) pandemic has had a substantial detrimental impact on mental health, especially depression, and this has led to a high incidence of suicidal ideation (SI) around the globe, with the pandemic's post-peak period seeing the highest incidence in young adults. This study aims to propose an effective non-intrusive method for early detection of SI in young adults utilizing depression as a biomarker in structural magnetic resonance imaging. This paper introduces a hybrid machine learning approach utilizing attention mechanisms and spiking neural networks to differentiate between depression patients without SI and healthy controls. The hybrid method successfully completed the classification task after stratified 5-fold cross-validation, achieving test accuracy, sensitivity, specificity, and area under curve of 94%, 100%, 92%, and 0.96, respectively. The proposed algorithms offer an objective tool for identifying early SI risk in depressed patients without suicidal thoughts, alongside clinical assessment. 2023 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/44290/1/Performance_Evaluation_of_Attention_Mechanism_and_Spiking_Neural_Networks_on_sMRI_Data_for_Suicide_Ideation_Assessment.pdf Corrine, Francis and Abdulrazak Yahya, Saleh (2023) Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment. In: 2023 IEEE International Conference on Computing (ICOCO), 9-12 Oct. 2023, Langkawi, Malaysia. https://ieeexplore.ieee.org/document/10397625
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 QA76 Computer software
spellingShingle QA76 Computer software
Corrine, Francis
Abdulrazak Yahya, Saleh
Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment
description The coronavirus disease 2019 (COVID-19) pandemic has had a substantial detrimental impact on mental health, especially depression, and this has led to a high incidence of suicidal ideation (SI) around the globe, with the pandemic's post-peak period seeing the highest incidence in young adults. This study aims to propose an effective non-intrusive method for early detection of SI in young adults utilizing depression as a biomarker in structural magnetic resonance imaging. This paper introduces a hybrid machine learning approach utilizing attention mechanisms and spiking neural networks to differentiate between depression patients without SI and healthy controls. The hybrid method successfully completed the classification task after stratified 5-fold cross-validation, achieving test accuracy, sensitivity, specificity, and area under curve of 94%, 100%, 92%, and 0.96, respectively. The proposed algorithms offer an objective tool for identifying early SI risk in depressed patients without suicidal thoughts, alongside clinical assessment.
format Proceeding
author Corrine, Francis
Abdulrazak Yahya, Saleh
author_facet Corrine, Francis
Abdulrazak Yahya, Saleh
author_sort Corrine, Francis
title Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment
title_short Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment
title_full Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment
title_fullStr Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment
title_full_unstemmed Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment
title_sort performance evaluation of attention mechanism and spiking neural networks on smri data for suicide ideation assessment
publishDate 2023
url http://ir.unimas.my/id/eprint/44290/1/Performance_Evaluation_of_Attention_Mechanism_and_Spiking_Neural_Networks_on_sMRI_Data_for_Suicide_Ideation_Assessment.pdf
http://ir.unimas.my/id/eprint/44290/
https://ieeexplore.ieee.org/document/10397625
_version_ 1789430384931373056
score 13.211869

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