Comparison of the WEKA and SVM-light based on support vector machine in classifying Alzheimer’s disease using structural features from brain MR imaging

Abstract

Abstract The aim was to compare the WEKA and SVM-light software based on support vector machine (SVM) algorithm using features from brain T1-weighted MRI for differentiating AD patients and normal elderly subjects. The FreeSurfer software was used to extract cerebral volumes and thicknesses from T1-weighted brain MRI (100 AD patients and 100 normal elderly subjects). Seven structures were selected based on literature reviews consisting of hippocampus and amygdala volume, entorhinal cortex thickness of both hemispheres, and total gray matter volume. Relative volume of hippocampus, amygdala, and total gray matter were normalized by total intracranial volume (TIV). Fifteen combinations of seven structures were applied as input features to WEKA and SVM-light. The receiver operating characteristic (ROC) analysis and area under the curve (AUC) were used to evaluate the classification performance. The combination of hippocampus relative volume and entorhinal cortex thickness provided the highest classification performance and the AUC values were 0.913 and 0.918 for WEKA and SVM-light, respectively. There was no statistically difference of the AUC values (p-value > 0.05) between two software using the same input features. In conclusion, there was no statistically difference between the use of WEKA and SVM-light software for differentiating AD patients and normal elderly subjects.