Classification of MRI and psychological testing data based on support vector machine.

Classification of MRI and psychological testing data based on support vector machine. Int J Clin Exp Med. 2017 Dec;10(12):16004-16026 Authors: Yang W, Chen X, Cohen DS, Rosin ER, Toga AW, Thompson PM, Huang X Abstract Alzheimer's disease (AD) is a progressive, and often fatal, brain disease that causes neurodegeneration, resulting in memory loss as well as other cognitive and behavioral problems. Here, we propose a novel multimodal method combining independent components from MRI measures and clinical assessments to distinguish Alzheimer's patients or mild cognitive impairment (MCI) subjects from healthy elderly controls. 70 AD subjects (mean age: 77.15 ± 6.2 years), 98 MCI subjects (mean age: 76.91 ± 5.7 years), and 150 HC subjects (mean age: 75.69 ± 3.8 years) were analyzed. Our method includes the following steps: pre-processing, estimating the number of independent components from the MR image data, extracting effective voxels for classification, and classification using a support vector machine (SVM)-based classifier. As a result, with regards to classifying AD from healthy controls, we achieved a classification accuracy of 97.7%, sensitivity of 99.2%, and specificity of 96.7%; for differentiating MCI from healthy controls, we achieved a classification accuracy of 87.8%, a sensitivity of 86.0%, and a specificity of 89.6; these results are better than those obtained with clinical measurements alone (accuracy of 79.5%, sensitiv...
Source: International Journal of Clinical and Experimental Medicine - Category: Drugs & Pharmacology Tags: Int J Clin Exp Med Source Type: research