# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 3 Experimental Results  
### Model Performance and Method Comparison  
The RACF proposed in this study demonstrated significant advantages in the classification task of PD versus healthy controls. Under five-fold cross-validation, the RACF model achieved an accuracy of 91.2% and an AUC of 0.94. In comparison to the Transformer network using only SNP data (accuracy of 80.1%) and the 3D DenseNet model using only MRI features (accuracy of 84.1%), RACF substantially enhanced classification performance. Table 2 compares the classification performance of single-modality and multimodal models under identical experimental conditions.  
In comparison to traditional machine learning methods, such as SVM, RF, XGBoost (XGB), and MLP, our proposed approach demonstrates superior performance. The accuracies of these traditional methods are 74.9%, 71.1%, 76.4%, and 80.9%, respectively. This performance gap may primarily be attributed to the inefficient handling of cross-modal heterogeneity in feature concatenation strategies.  
Moreover, RACF exhibited clear advantages when compared to current multimodal frameworks. The static weighted fusion model proposed by [PERSON] et al. and the hybrid network by [PERSON] et al. demonstrated inferior classification performance compared to RACF. In terms of key evaluation metrics such as accuracy, AUC, precision, and F1 score, RACF outperformed both models. For instance, the model of [PERSON] et al. achieved an accuracy of 86.4%, while the model of [PERSON] et al. reached 82.7%. In contrast, the accuracy of RACF was 91.2%, highlighting its superiority in handling multimodal data. PIDGN, which uses a dynamic feature fusion strategy through a gated attention mechanism, also showed strong performance with an accuracy of 89.5%. However, RACF still outperformed PIDGN across all key evaluation metrics, including accuracy, precision, recall, F1 score, and AUROC. Specifically, PIDGN achieved an accuracy of 89.5%, while RACF achieved 91.2%, demonstrating the better ability of RACF to leverage multimodal data and improve classification performance. Table 3 presents a detailed comparison of the classification performance of different models on multimodal data. RACF excels across all evaluation metrics, achieving an accuracy of 91.2%, precision of 85.9%, recall of 89.5%, F1 score of 94.3%, and AUROC of 0.941.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Data** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline MRI & 3D DenseNet & 0.841 & 0.864 & 0.822 & 0.831 \\ SNP & Transformer & 0.801 & 0.767 & 0.958 & 0.852 \\ Fusion & Ours & 0.912 & 0.819 & 0.807 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification performance comparison between single-modality and multimodal models.  
To further validate the stability and robustness of the RACF model, Figure 4 presents five independent ROC curves from the five-fold cross-validation. These curves nearly overlap, indicating that RACF exhibits consistent stability across different training and testing data splits, demonstrating a strong generalization ability.