Group,Method,Input,accuracy,balanced_accuracy,macro_precision,macro_recall,macro_f1,macro_auc,macro_f1_CI
Metadata,XGBoost,Tabular,52.4±6.6,47.3±6.6,49.0±6.1,47.3±6.6,47.4±6.5,66.7±7.4,"[43.5, 52.3]"
Metadata,MLP,Tabular,41.6±10.4,44.2±9.2,41.6±9.5,44.2±9.2,38.9±9.7,59.1±9.1,"[36.2, 44.4]"
CNN,ResNet50,2D,44.6±7.4,39.9±6.8,39.1±8.4,39.9±6.8,36.8±7.4,57.6±6.9,"[35.3, 43.2]"
CNN,DenseNet121,2.5D,51.4±6.9,46.8±7.6,47.3±10.8,46.8±7.6,44.9±7.7,67.9±6.6,"[41.3, 49.6]"
CNN,3D ResNet18,3D,49.2±4.9,41.3±6.2,34.6±11.4,41.3±6.2,35.4±6.8,67.2±5.6,"[36.4, 44.7]"
Transformer,ViT-B/16,2.5D,53.7±5.9,49.0±7.8,49.6±10.5,49.0±7.8,46.2±8.5,69.5±4.9,"[44.2, 52.7]"
Transformer,3D Swin-T,3D,48.1±6.8,44.2±6.7,43.7±11.9,44.2±6.7,40.3±8.5,67.9±4.3,"[39.2, 47.7]"
Hybrid,3D HCCT,3D,51.9±6.2,45.0±7.5,41.0±9.6,45.0±7.5,40.7±7.8,64.4±5.5,"[40.7, 49.5]"
Hybrid,CNN-VSwinFormer-lite,3D,43.8±6.8,40.0±6.5,37.0±10.1,40.0±6.5,35.4±7.8,63.2±5.3,"[34.8, 43.1]"
Multimodal,DenseNet + concat,MRI+tab,54.6±7.0,52.5±6.6,52.1±9.3,52.5±6.6,49.7±6.2,71.0±4.7,"[45.9, 54.3]"
Proposed,TriFuse-AD,MRI+tab,53.2±7.3,50.8±6.5,50.7±7.5,50.8±6.5,48.8±6.6,69.2±6.4,"[45.8, 54.0]"