In this work, we developed an automated ML algorithm based on multimodal MRI capable of discriminating the most common forms of dementia. The performance of this classifier was validated using quality metrics that resulted in high scores for accuracy, macro-precision, macro-recall, macro-F1 and AUC. The classifier was successful in discriminating between the 4 groups (AD, FTD, DLB and CN) characterized by different neuropsychological scores and ApoE expression (Table 2). The algorithm selected CDR, age, gender information, MRI-based diffusion metrics, volumetric and cortical thickness values as the best differentiating features.
SVM performance did not differ significantly between the test and training sets using 22 informative features; and performances on training set were higher than performance on the test set arguing against severe overfitting56.
In the test set group, MUQUBIA…
