# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 3 Experimental Results
### Abnormal Brain Regions and Pathogenic Gene Analysis  
We further investigated the interpretability of RACF in PD diagnosis. By utilizing Grad-CAM, we visualized the key brain regions in sMRI images that drive the predictions of the model. Figure 5 highlights these critical regions, with the model primarily focusing on the basal ganglia and substantia nigra, particularly the caudate nucleus and globus pallidus within the basal ganglia. Figure 6 presents the SHAP value analysis, identifying the top 20 SNPs that contribute the most to the classification decision. In the figure, blue represents SNPs that have been reported in existing studies, while red indicates SNPs predicted as risk loci by our method. Among the top 20 SNPs ranked by SHAP values, rs11230569, rs356220, rs1564282, rs823156, rs356219, rs242557, rs17649553, rs12185268, rs2736990, rs11248051, and rs947211 are all known PD-related loci reported in the literature.  
Figure 4: The ROC curve of the RACF model evaluated using five-fold cross-validation. The points on the ROC curve correspond to the cut-off points that maximize [PERSON]’s index. The values represent [PERSON]’s index at these points, along with the coordinates on the curve. The x-axis represents the false positive rate (FPR = 1 - specificity), and the y-axis represents the true positive rate (TPR = sensitivity).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SVM [21] & 0.749 & 0.729 & 0.713 & 0.844 \\ RF [22] & 0.711 & 0.744 & 0.894 & 0.848 \\ XGB [23] & 0.764 & 0.751 & 0.853 & 0.807 \\ MLP [24] & 0.809 & 0.843 & 0.752 & 0.818 \\ [PERSON] et al. [19] & 0.864 & 0.814 & 0.802 & 0.815 \\ [PERSON] et al. [20] & 0.827 & 0.778 & 0.876 & 0.836 \\ PIDGN [17] & 0.895 & 0.848 & 0.903 & 0.912 \\ Ours & 0.912 & 0.859 & 0.895 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification performance of different models on multimodal data.  
rs78251200, rs4538475, rs356228, rs11724635, rs6595513, rs4698412, rs1013496, rs11711441, and rs4771268 are potential risk loci predicted by the RACF model.  
The key SNP loci identified through SHAP value analysis were further annotated using the ANNOVAR tool ([[https://annovar.openbioinformatics.org](https://annovar.openbioinformatics.org)]([https://annovar.openbioinformatics.org](https://annovar.openbioinformatics.org)), accessed on 20 January 2025), with the detailed results provided in Table 4. From the table, it is evident that the biological functions of most selected SNP loci align closely with the core pathological mechanisms of PD.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**SNP** & **Chromosome** & **Gene** & **Function** \\ \hline rs11230569 & 11 & SLC41A1 & The balance of magnesium ions in the brain \\ rs78251200 & 11 & SAAL1 & Immune regulation \\ rs356220 & 4 & SNCA & Accumulation of alpha-synuclein \\ rs1564282 & 4 & GAK & Regulation of cell division and microtubule stability \\ rs823156 & 11 & SLC41A1 & The balance of magnesium ions in the brain \\ rs4538475 & 4 & BST1 & Intracellular calcium ion regulation \\ rs356219 & 4 & SNCA & Accumulation of alpha-synuclein \\ rs356182 & 4 & LOC124900602 & Mitochondrial regulation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gene annotation of top 20 SNPs using ANNOVAR.  
Figure 5: The activation map for predicting “PD,” showing a slice from three different directions of a randomly selected sample. The highlighted areas correspond to the central regions of the brain, which roughly align with the areas of core pathological changes.  
Figure 6: Top 20 contributing SNPs by RACF model for PD. Blue: reported SNPs; Red: newly predicted risk loci.