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
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | COVID-19 |
| Uncontrolled Keywords: | COVID-19; Depression; Machine learning; Mental health intervention; Structural magnetic resonance imaging; Suicide ideation; Young adults. |
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development Faculties, Institutes, Centres > Faculty of Cognitive Sciences and Human Development Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development |
| Depositing User: | Saleh Al-Hababi |
| Date Deposited: | 02 Dec 2024 06:11 |
| Last Modified: | 02 Dec 2024 06:11 |
| URI: | http://ir.unimas.my/id/eprint/46777 |
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