In this study, we aimed to classify MR images for recognizing Alzheimer Disease (AD) in a group of patients
who were recently diagnosed by clinical history and neuropsychiatric exams by using non-biased machine-learning techniques.
T1 weighted MRI scans of 31 patients with probable AD and 31 age- and gender-matched cognitively normal elderly
were analyzed with voxel-based morphometry and classified by support vector machine (SVM), a machine learning
technique. SVM could differentiate patients from controls with accuracy of 74 % (sensitivity: 70 % and specificity: 77 %)
when the whole brain was included the analyses. The classification accuracy was increased to 79 % (sensitivity: 65 % and
specificity: 93 %) when the analyses restricted to hippocampus. Our results showed that SVM is a promising tool for diagnosis
of AD, but needed to be improved.
Keywords: Alzheimer’s disease, classification, diagnoses, support vector machines, hippocampus, magnetic resonance
imaging, hippocampus, cardiovascular disease.
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