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
• DOI: 10.1371/journal.pone.0194479
• ISSN: 1932-6203

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.