Fatma PolatSelcuk Orhan DemirelOmer KitisFatma SimsekDamla Isman HaznedarogluKerry CoburnEmre KumralAli Saffet Gonul2025-10-0620121567-205010.2174/156720512802455359http://dx.doi.org/10.2174/156720512802455359https://gcris.yasar.edu.tr/handle/123456789/7780In 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.EnglishAlzheimer's disease, classification, diagnoses, support vector machines, hippocampus, magnetic resonance imagingDIAGNOSIS, DISEASE, ATROPHY, PATTERNS, ADArticle