Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis
Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effective early diagnostic methods is thus critic...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03270-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849333404352380928 |
|---|---|
| author | Qian Zhang Jinhua Sheng Rougang Zhou Qiao Zhang Binbing Wang Rong Zhang |
| author_facet | Qian Zhang Jinhua Sheng Rougang Zhou Qiao Zhang Binbing Wang Rong Zhang |
| author_sort | Qian Zhang |
| collection | DOAJ |
| description | Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effective early diagnostic methods is thus critical for improving patient outcomes and slowing disease progression. In this paper, an enhanced Particle Swarm Optimization (PSO) algorithm, which integrates opposition-based Latin squares sampling initialization (OL) with dynamic inertia weights and learning factors (D), termed OLDPSO, is proposed to improve feature selection and classification within a Support Vector Machine (SVM) model for AD diagnosis using magnetic resonance imaging (MRI) data. MRI, as a non-invasive modality, reveals structural brain changes, particularly in gray matter (GM) and white matter (WM) volumes, which are key biomarkers for AD. However, extracting essential features from complex GM and WM data remains a significant challenge. To address this, the proposed OLDPSO, which adaptively balances global exploration and local exploitation, overcomes traditional PSO limitations. Benchmark experiments show that OLDPSO outperforms existing PSO variants in solution quality and convergence speed. Validated with data from the AD Neuroimaging Initiative (ADNI), the OLDPSO-SVM model demonstrates superior performance in differentiating AD, mild cognitive impairment (MCI), and normal control (NC) groups, particularly in classifying MCI subtypes (MCI-NC and MCI-C). Results show that combining GM and WM features yields higher diagnostic accuracy than using either alone, and the model identified key brain regions associated with AD progression. Specifically, the model achieved accuracies of 99.11%, 89.76%, 99.07%, 88.38%, 94.69%, and 87.96% in the diagnosis of AD vs. NC, NC vs. MCI-NC, NC vs. MCI-C, MCI-NC vs. MCI-C, MCI-NC vs. AD, and MCI-C vs. AD, respectively. Through optimized feature selection, the OLDPSO-SVM model enhances diagnostic performance and provides valuable insights for developing MRI-based multimodal diagnostic tools for AD. |
| format | Article |
| id | doaj-art-7d3fe91fbb714d8db87a8b17f55af9cf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7d3fe91fbb714d8db87a8b17f55af9cf2025-08-20T03:45:52ZengNature PortfolioScientific Reports2045-23222025-07-0115113510.1038/s41598-025-03270-7Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosisQian Zhang0Jinhua Sheng1Rougang Zhou2Qiao Zhang3Binbing Wang4Rong Zhang5School of Computer Science and Technology, Hangzhou Dianzi UniversitySchool of Computer Science and Technology, Hangzhou Dianzi UniversityKey Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of P. R. ChinaBeijing HospitalSchool of Computer Science and Technology, Hangzhou Dianzi UniversitySchool of Computer Science and Technology, Hangzhou Dianzi UniversityAbstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effective early diagnostic methods is thus critical for improving patient outcomes and slowing disease progression. In this paper, an enhanced Particle Swarm Optimization (PSO) algorithm, which integrates opposition-based Latin squares sampling initialization (OL) with dynamic inertia weights and learning factors (D), termed OLDPSO, is proposed to improve feature selection and classification within a Support Vector Machine (SVM) model for AD diagnosis using magnetic resonance imaging (MRI) data. MRI, as a non-invasive modality, reveals structural brain changes, particularly in gray matter (GM) and white matter (WM) volumes, which are key biomarkers for AD. However, extracting essential features from complex GM and WM data remains a significant challenge. To address this, the proposed OLDPSO, which adaptively balances global exploration and local exploitation, overcomes traditional PSO limitations. Benchmark experiments show that OLDPSO outperforms existing PSO variants in solution quality and convergence speed. Validated with data from the AD Neuroimaging Initiative (ADNI), the OLDPSO-SVM model demonstrates superior performance in differentiating AD, mild cognitive impairment (MCI), and normal control (NC) groups, particularly in classifying MCI subtypes (MCI-NC and MCI-C). Results show that combining GM and WM features yields higher diagnostic accuracy than using either alone, and the model identified key brain regions associated with AD progression. Specifically, the model achieved accuracies of 99.11%, 89.76%, 99.07%, 88.38%, 94.69%, and 87.96% in the diagnosis of AD vs. NC, NC vs. MCI-NC, NC vs. MCI-C, MCI-NC vs. MCI-C, MCI-NC vs. AD, and MCI-C vs. AD, respectively. Through optimized feature selection, the OLDPSO-SVM model enhances diagnostic performance and provides valuable insights for developing MRI-based multimodal diagnostic tools for AD.https://doi.org/10.1038/s41598-025-03270-7Alzheimer’s diseaseParticle swarm optimizationSupport vector machineFeature selectionGray matterWhite matter |
| spellingShingle | Qian Zhang Jinhua Sheng Rougang Zhou Qiao Zhang Binbing Wang Rong Zhang Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis Scientific Reports Alzheimer’s disease Particle swarm optimization Support vector machine Feature selection Gray matter White matter |
| title | Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis |
| title_full | Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis |
| title_fullStr | Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis |
| title_full_unstemmed | Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis |
| title_short | Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis |
| title_sort | enhanced particle swarm optimization for feature selection in svm based alzheimer s disease diagnosis |
| topic | Alzheimer’s disease Particle swarm optimization Support vector machine Feature selection Gray matter White matter |
| url | https://doi.org/10.1038/s41598-025-03270-7 |
| work_keys_str_mv | AT qianzhang enhancedparticleswarmoptimizationforfeatureselectioninsvmbasedalzheimersdiseasediagnosis AT jinhuasheng enhancedparticleswarmoptimizationforfeatureselectioninsvmbasedalzheimersdiseasediagnosis AT rougangzhou enhancedparticleswarmoptimizationforfeatureselectioninsvmbasedalzheimersdiseasediagnosis AT qiaozhang enhancedparticleswarmoptimizationforfeatureselectioninsvmbasedalzheimersdiseasediagnosis AT binbingwang enhancedparticleswarmoptimizationforfeatureselectioninsvmbasedalzheimersdiseasediagnosis AT rongzhang enhancedparticleswarmoptimizationforfeatureselectioninsvmbasedalzheimersdiseasediagnosis |