SVM Based AutoEncoder for Detecting Dementia in Young Adults
- Title
- SVM Based AutoEncoder for Detecting Dementia in Young Adults
- Creator
- Sharma V.; Midhunchakkaravarthy D.
- Description
- Dementia's impact on cognitive function necessitates timely diagnosis for effective intervention. Understanding the need for timely detection, the proposed work integrates SVM's decision boundary determination and autoencoder's noise reduction capabilities. The proposed work advances in dementia detection in young adult. Results indicate promising performance, with the model achieving high accuracy around 85.33%. The ROC curve illustrates a balanced trade-off between sensitivity and specificity, while the precision-recall curve highlights effective classification. Importantly, the model surpasses existing literature, underscoring its practical utility. While acknowledging limitations, such as parameter fine-tuning, this study lays the groundwork for refining and expanding this innovative methodology. In summary, this research contributes to the urgent field of early dementia detection, potentially transforming patient care and intervention strategies. 2023 IEEE.
- Source
- Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 2516-2520.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Dementia detection; Explainability; Explainable AI; LIME; SHAP
- Coverage
- Sharma V., Lincoln University College, Department of Computer Science and Multimedia, Kuala Lumpur, Malaysia, CHRIST (Deemed to Be University), Delhi-NCR, India; Midhunchakkaravarthy D., Lincoln University College, Department of Computer Science and Multimedia, Kuala Lumpur, Malaysia
- Rights
- Restricted Access
- Relation
- ISBN: 979-835030448-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sharma V.; Midhunchakkaravarthy D., “SVM Based AutoEncoder for Detecting Dementia in Young Adults,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 4, 2025, https://archives.christuniversity.in/items/show/19764.