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Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults

Accurate assessment of suicide ideation (SI) risk in depressed young adults remains a critical challenge, with existing methods exhibiting limited effectiveness. This study proposes a novel machine learning approach leveraging Evolving Spiking Neural Networks (ESNN) to enhance SI risk detection util...

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Main Authors: Corrine, Francis, Abdulrazak Yahya, Saleh
Format: Thesis
Language:English
English
English
Published: Institute of Advanced Engineering and Science (IAES) 2024
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/45396/3/DSVA_Corrine%20Francis.pdf
http://ir.unimas.my/id/eprint/45396/4/Thesis%20Master_%20Corrine%20Francis%20-%2024%20pages.pdf
http://ir.unimas.my/id/eprint/45396/5/Thesis%20Master_%20Corrine%20Francis.ftext.pdf
http://ir.unimas.my/id/eprint/45396/
https://ijai.iaescore.com/index.php/IJAI/index
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spelling my.unimas.ir.453962024-07-30T07:26:39Z http://ir.unimas.my/id/eprint/45396/ Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults Corrine, Francis Abdulrazak Yahya, Saleh Q Science (General) T Technology (General) Accurate assessment of suicide ideation (SI) risk in depressed young adults remains a critical challenge, with existing methods exhibiting limited effectiveness. This study proposes a novel machine learning approach leveraging Evolving Spiking Neural Networks (ESNN) to enhance SI risk detection utilizing structural magnetic resonance imaging (sMRI) data. ESNNs, inspired by the brain's information processing mechanisms, excel at capturing temporal and spatial patterns in data, making them well-suited for modeling the complex dynamics of SI risk factors. Unlike traditional neural networks, ESNNs employ spiking neurons and adaptive learning mechanisms that continuously update internal representations, enhancing their robustness to changing risk factors and individual SI trajectories. However, their application in SI detection has been largely underexplored, creating a gap in leveraging their unique capabilities for this critical task. To address this gap, the self-AM-ESNN model is introduced, which integrates self-attention mechanisms (self-AM) with ESNN to enable effective feature extraction and learning from sMRI data. By integrating self-AM with ESNN's dynamic learning capabilities, the model can capture complex neuroanatomical patterns associated with SI risk while adapting to individual variations. Evaluated on a dataset of 20 depressed individuals and 60 healthy controls, the self-AM-ESNN model demonstrated exceptional performance in classifying depression, achieving 94% test accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These promising results highlight the potential of ESNN-based approaches to augment clinical decision-making and mental health interventions for SI risk assessment. Furthermore, the study incorporates a user-centric evaluation framework that enables mental health professionals and service users to assess the model's detections and rationale, facilitating informed decision-making processes. By providing interpretable insights into the underlying factors contributing to SI risk, this approach empowers stakeholders to make more informed choices and tailor interventions accordingly. Institute of Advanced Engineering and Science (IAES) 2024-07-02 Thesis NonPeerReviewed text en http://ir.unimas.my/id/eprint/45396/3/DSVA_Corrine%20Francis.pdf text en http://ir.unimas.my/id/eprint/45396/4/Thesis%20Master_%20Corrine%20Francis%20-%2024%20pages.pdf text en http://ir.unimas.my/id/eprint/45396/5/Thesis%20Master_%20Corrine%20Francis.ftext.pdf Corrine, Francis and Abdulrazak Yahya, Saleh (2024) Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults. Masters thesis, IAES International Journal of Artificial Intelligence (IJ-AI). https://ijai.iaescore.com/index.php/IJAI/index ISSN/e-ISSN 2089-4872/2252-8938
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
English
English
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Corrine, Francis
Abdulrazak Yahya, Saleh
Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults
description Accurate assessment of suicide ideation (SI) risk in depressed young adults remains a critical challenge, with existing methods exhibiting limited effectiveness. This study proposes a novel machine learning approach leveraging Evolving Spiking Neural Networks (ESNN) to enhance SI risk detection utilizing structural magnetic resonance imaging (sMRI) data. ESNNs, inspired by the brain's information processing mechanisms, excel at capturing temporal and spatial patterns in data, making them well-suited for modeling the complex dynamics of SI risk factors. Unlike traditional neural networks, ESNNs employ spiking neurons and adaptive learning mechanisms that continuously update internal representations, enhancing their robustness to changing risk factors and individual SI trajectories. However, their application in SI detection has been largely underexplored, creating a gap in leveraging their unique capabilities for this critical task. To address this gap, the self-AM-ESNN model is introduced, which integrates self-attention mechanisms (self-AM) with ESNN to enable effective feature extraction and learning from sMRI data. By integrating self-AM with ESNN's dynamic learning capabilities, the model can capture complex neuroanatomical patterns associated with SI risk while adapting to individual variations. Evaluated on a dataset of 20 depressed individuals and 60 healthy controls, the self-AM-ESNN model demonstrated exceptional performance in classifying depression, achieving 94% test accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These promising results highlight the potential of ESNN-based approaches to augment clinical decision-making and mental health interventions for SI risk assessment. Furthermore, the study incorporates a user-centric evaluation framework that enables mental health professionals and service users to assess the model's detections and rationale, facilitating informed decision-making processes. By providing interpretable insights into the underlying factors contributing to SI risk, this approach empowers stakeholders to make more informed choices and tailor interventions accordingly.
format Thesis
author Corrine, Francis
Abdulrazak Yahya, Saleh
author_facet Corrine, Francis
Abdulrazak Yahya, Saleh
author_sort Corrine, Francis
title Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults
title_short Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults
title_full Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults
title_fullStr Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults
title_full_unstemmed Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults
title_sort leveraging smri, self-attention mechanisms, and evolving spiking neural networks for enhanced suicide ideation detection in depressed young adults
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2024
url http://ir.unimas.my/id/eprint/45396/3/DSVA_Corrine%20Francis.pdf
http://ir.unimas.my/id/eprint/45396/4/Thesis%20Master_%20Corrine%20Francis%20-%2024%20pages.pdf
http://ir.unimas.my/id/eprint/45396/5/Thesis%20Master_%20Corrine%20Francis.ftext.pdf
http://ir.unimas.my/id/eprint/45396/
https://ijai.iaescore.com/index.php/IJAI/index
_version_ 1806456051455754240
score 13.211869

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