Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease

• Published in: PloS one Vol. 13; no. 3; p. e0194479
• Main Authors: Bi, Xia-an, Shu, Qing, Sun, Qi, Xu, Qian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.03.2018; Public Library of Science (PLoS)
• Subjects: Accuracy; Alzheimer's disease; Artificial intelligence; Biology and Life Sciences; Brain; Brain mapping; Brain research; Care and treatment; Classification; Cluster analysis; Clusters; Computer and Information Sciences; Cortex (cingulate); Cortex (frontal); Dementia; Diagnosis; Engineering; Frontal gyrus; Functional magnetic resonance imaging; Hospitals; Information science; Magnetic resonance imaging; Medical imaging; Medicine and Health Sciences; Neurodegenerative diseases; Neuroimaging; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Precentral gyrus; Research and Analysis Methods; Support vector machines; China; United States > US; California

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. B...