Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

Background: The determination of brain volumes using visual ratings is inherently low accuracy for the diagnosis of the Alzheimer’s disease (AD). A support-vector machine (SVM) is one of machine learning techniques which may be utilized as classifier for various classification problems. This study exploratorily investigated the accuracy of SVM-classification models for AD subjects using brain volume and various clinical data as features. Methods: The study was designed as a retrospective chart review. The total of 201 eligible subjects were recruited from the Memory Clinic at Siriraj Hospital, Thailand. Eighteen cases were excluded due to incomplete MRI data. Subjects were randomly assigned to a training group (AD=46, normal = 46) and testing group (AD=45, normal =46) for SVM modeling and validation, respectively. The results in terms of accuracy and a receiver operating characteristic curve analysis are reported. Results: The highest accuracy for brain volumetry (62.64%) was found using hippocampus as single feature. A combination clinical parameters as features provided accuracy ranging between 83%–90%. However, a combination of brain volumetry and clinical parameters as features to the SVM models did not improved the accuracy of the results. Conclusions: In our study, the use of brain volumetry as SVM features provided low classification accuracy with the highest accuracy of 62.64% using the hippocampus volume alone. In contrast, the use of clinical parameters (Thai mental state examination score, controlled oral word association tests (animals; and letters K, S and P), learning memory, clock-drawing test, and construction-praxis) as features for SVM models provided good accuracy between 83%–90%.