# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 1 Introduction  
Parkinson's disease (PD), also known as paralysis agitans, is a prevalent neurodegenerative disorder with an increasing incidence worldwide. Epidemiological data indicate that more than 10 million individuals are affected by PD, and its prevalence has more than doubled in the past 25 years, making it the second most common neurodegenerative disease after Alzheimer's disease (AD) [1, 2, 3]. The core clinical symptoms of PD include bradykinesia, resting tremor, muscle rigidity, and postural instability [4]. Traditionally, the diagnosisof PD has relied heavily on clinician-administered observational assessments, such as the Unified Parkinson's Disease Rating Scale (UPDRS) [5] or handwriting analysis [6]. However, these traditional methods are limited by significant inter-rater variability, temporal inconsistencies in symptom assessment, and insufficient sensitivity for detecting early-stage or prodromal manifestations of the disease. With the advancement of biomedical technologies, objective biomarker-driven tools such as electroencephalography (EEG) [7], magnetoencephalography (MEG), structural and functional magnetic resonance imaging (sMRI and fMRI) [8], and positron emission tomography (PET) [9] are increasingly integrated into clinical practice. These modalities complement conventional clinical assessments by quantifying neuroelectrical activity, anatomical morphology, and functional/metabolic dynamics, thereby elevating diagnostic precision. However, neurodegenerative disorders like PD and AD cause irreversible neural damage, underscoring the critical need for early identification and intervention. Genetic biomarkers, particularly single nucleotide polymorphisms (SNPs), offer molecular-level insights that enable pre-symptomatic risk stratification and personalized therapeutic planning, effectively bridging the gap between macroscopic imaging findings and subclinical pathological processes. In this study, we hypothesize that a deep learning framework integrating SNP profiles with sMRI data could establish a robust multimodal diagnostic paradigm for PD. SNPs capture disease-associated genetic susceptibility at the molecular scale, while sMRI detects macroscale neuroanatomical alterations, forming complementary cross-scale analytical dimensions. By leveraging artificial intelligence technology, it may not only enhance diagnostic accuracy but also decipher latent genotype-phenotype correlations, potentially uncovering novel biomarkers for disease progression monitoring. The evolution of deep learning technologies has redefined intelligent decision-making paradigms in medical practice, catalyzing a precision medicine revolution that shifts from experience-driven to data-driven approaches while markedly enhancing end-to-end efficacy in disease prediction, diagnosis, and therapeutic intervention. In melanoma detection, [PERSON] et al. [10] employed a vision transformer-based approach for classifying skin lesions, achieving notable accuracy in distinguishing malignant cases. Similarly, breast cancer diagnostics have benefited from machine learning techniques applied to mammography images, where advanced feature extraction and classification methods significantly improved diagnostic precision [11]. Within PD diagnosis, deep learning methods have emerged as particularly effective tools. Convolutional neural networks (CNNs), for instance, have excelled in analyzing 2D MRI slices for PD detection [12]. Further innovations include [PERSON] et al. [13], who combined voxel-based morphometry with classifier ensembles to enhance diagnostic reliability, and [PERSON] et al. [14], who enhanced robustness through architectural optimization of CNNs paired with median filtering. [PERSON] et al. [15] advanced this field further with their automated model, NP-PHOG-MFSMCIMV, which leveraged multi-dimensional feature analysis of brain MRI data to achieve precise symptom classification. In terms of multimodal strategies, [PERSON] et al.