Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier

► MRI data from multiple scanners was used to asses performance of disease classifier. ► Larger training sets led to higher performance and smaller confidence intervals. ► Scanning subjects on different systems introduced variance. ► No systematic effect due to change in field strength was found. ► For the presented setting, pooling of data was beneficial for the performance.