Trial reportPloS one2018
Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.
Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.Human brain mapping · 2021Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment.GeroScience · 2026Article
- Precise diagnosis of Alzheimer's disease based on sex-specific gray matter characteristics.Frontiers in neuroscience · 2026Article
- Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.Frontiers in neuroscience · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model.iScience · 2023Article
- Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases.Brain sciences · 2022Article
- Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease.Brain imaging and behavior · 2022Article
- Predictive classification of Alzheimer's disease using brain imaging and genetic data.Scientific reports · 2022Article
- Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning.Computational intelligence and neuroscience · 2022Article
- Identification of Pathogenetic Brain RegionsFrontiers in neuroscience · 2022Article
- Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest.Genes · 2021Article
- Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging.Frontiers in cardiovascular medicine · 2021Article
- Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.IEEE transactions on bio-medical engineering · 2020Article
- A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.NeuroImage · 2020Article
- Analysis of Alzheimer's Disease Based on the Random Neural Network Cluster in fMRI.Frontiers in neuroinformatics · 2018Article
- Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.Frontiers in physiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very high and generally less than 90%. The method of random support vector machine cluster was proposed to classify AD and HC in this paper. From the Alzheimer's Disease Neuroimaging Initiative database, the subjects including 25 AD individuals and 35 HC individuals were obtained. The classification accuracy could reach to 94.44% in the results. Furthermore, the method could also be used for feature selection and the accuracy could be maintained at the level of 94.44%. In addition, we could also find out abnormal brain regions (inferior frontal gyrus, superior frontal gyrus, precentral gyrus and cingulate cortex). It is worth noting that the proposed random support vector machine cluster could be a new insight to help the diagnosis of AD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.