HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript
Alzheimer’s dementia (AD) affects memory, language, and cognition and worsens over time. Therefore, it is critical to develop a reliable method for early detection of permanent brain atrophy and cognitive impairment. This study used clinical transcripts, a text-based adaptation of the ori...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9956807/ |
| _version_ | 1811293823851036672 |
|---|---|
| author | Yusera Farooq Khan Baijnath Kaushik Mohammad Khalid Imam Rahmani Md. Ezaz Ahmed |
| author_facet | Yusera Farooq Khan Baijnath Kaushik Mohammad Khalid Imam Rahmani Md. Ezaz Ahmed |
| author_sort | Yusera Farooq Khan |
| collection | DOAJ |
| description | Alzheimer’s dementia (AD) affects memory, language, and cognition and worsens over time. Therefore, it is critical to develop a reliable method for early detection of permanent brain atrophy and cognitive impairment. This study used clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer’s patients. This audio transcript data were taken from DementiaBank, which is the largest public dataset of AD transcripts. This study aims to show how Transfer Learning-based models and swarm intelligence optimization techniques can be used to predict Alzheimer’s disease. To enhance the prediction performance for Alzheimer’s disease, a hybrid swarm intelligence linguistic feature selection (HSI-LFS) approach is proposed that extracts a combined feature set using Particle Swarm Optimization (PSO), Dragonfly Optimization (DO), and Grey Wolf Optimization (GWO) algorithms. In addition, a transfer learning-based model called HSI-LFS-BERT, a combination of the HSI-LFS feature selection method and Bidirectional Encoder Representations from Transformer (BERT) algorithm, is proposed. The proposed model was compared using two feature sets: the first set consisted of the initial feature set and the second set contained a hybrid feature set that was extracted using the suggested HSI-LFS method. BERT embedding with HSI-LFS outperformed the conventional feature set, providing the most accurate modeling parameters while reducing the computations by 27.19%. The proposed HSI-LFS-BERT model outperformed state-of-the-art models, achieving 98.24% accuracy, 91.56% precision, and 98.78% recall. |
| first_indexed | 2024-04-13T05:08:18Z |
| format | Article |
| id | doaj.art-7c6ebc23b995464f9f79376bd4d91f10 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T05:08:18Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7c6ebc23b995464f9f79376bd4d91f102022-12-22T03:01:07ZengIEEEIEEE Access2169-35362022-01-011012699012700410.1109/ACCESS.2022.32236819956807HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio TranscriptYusera Farooq Khan0Baijnath Kaushik1https://orcid.org/0000-0003-2083-6924Mohammad Khalid Imam Rahmani2https://orcid.org/0000-0002-1937-7145Md. Ezaz Ahmed3https://orcid.org/0000-0002-7925-8260School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, IndiaSchool of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, IndiaCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaAlzheimer’s dementia (AD) affects memory, language, and cognition and worsens over time. Therefore, it is critical to develop a reliable method for early detection of permanent brain atrophy and cognitive impairment. This study used clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer’s patients. This audio transcript data were taken from DementiaBank, which is the largest public dataset of AD transcripts. This study aims to show how Transfer Learning-based models and swarm intelligence optimization techniques can be used to predict Alzheimer’s disease. To enhance the prediction performance for Alzheimer’s disease, a hybrid swarm intelligence linguistic feature selection (HSI-LFS) approach is proposed that extracts a combined feature set using Particle Swarm Optimization (PSO), Dragonfly Optimization (DO), and Grey Wolf Optimization (GWO) algorithms. In addition, a transfer learning-based model called HSI-LFS-BERT, a combination of the HSI-LFS feature selection method and Bidirectional Encoder Representations from Transformer (BERT) algorithm, is proposed. The proposed model was compared using two feature sets: the first set consisted of the initial feature set and the second set contained a hybrid feature set that was extracted using the suggested HSI-LFS method. BERT embedding with HSI-LFS outperformed the conventional feature set, providing the most accurate modeling parameters while reducing the computations by 27.19%. The proposed HSI-LFS-BERT model outperformed state-of-the-art models, achieving 98.24% accuracy, 91.56% precision, and 98.78% recall.https://ieeexplore.ieee.org/document/9956807/HSI-LFSmachine learningcomputational intelligenceparticle swarm optimization (PSO)dragonfly optimization (DO)grey wolf optimization (GWO) |
| spellingShingle | Yusera Farooq Khan Baijnath Kaushik Mohammad Khalid Imam Rahmani Md. Ezaz Ahmed HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript IEEE Access HSI-LFS machine learning computational intelligence particle swarm optimization (PSO) dragonfly optimization (DO) grey wolf optimization (GWO) |
| title | HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript |
| title_full | HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript |
| title_fullStr | HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript |
| title_full_unstemmed | HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript |
| title_short | HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript |
| title_sort | hsi lfs bert novel hybrid swarm intelligence based linguistics feature selection and computational intelligent model for alzheimer x2019 s prediction using audio transcript |
| topic | HSI-LFS machine learning computational intelligence particle swarm optimization (PSO) dragonfly optimization (DO) grey wolf optimization (GWO) |
| url | https://ieeexplore.ieee.org/document/9956807/ |
| work_keys_str_mv | AT yuserafarooqkhan hsilfsbertnovelhybridswarmintelligencebasedlinguisticsfeatureselectionandcomputationalintelligentmodelforalzheimerx2019spredictionusingaudiotranscript AT baijnathkaushik hsilfsbertnovelhybridswarmintelligencebasedlinguisticsfeatureselectionandcomputationalintelligentmodelforalzheimerx2019spredictionusingaudiotranscript AT mohammadkhalidimamrahmani hsilfsbertnovelhybridswarmintelligencebasedlinguisticsfeatureselectionandcomputationalintelligentmodelforalzheimerx2019spredictionusingaudiotranscript AT mdezazahmed hsilfsbertnovelhybridswarmintelligencebasedlinguisticsfeatureselectionandcomputationalintelligentmodelforalzheimerx2019spredictionusingaudiotranscript |