Characterising the heterogeneous presentation of Parkinson's disease (PD) requires integrating biological and clinical markers within a unified predictive framework. While multimodal data provide complementary information, many existing computational models struggle with interpretability, class imbalance, or effective fusion of high-dimensional imaging and tabular clinical features. To address these limitations, we propose the Class-Weighted Sparse-Attention Fusion Network (SAFN), an interpretable deep learning framework for robust multimodal profiling. SAFN integrates MRI cortical thickness, MRI volumetric measures, clinical assessments, and demographic variables using modality-specific encoders and a symmetric cross-attention mechanism that captures nonlinear interactions between imaging and clinical representations. A sparsity-constrained attention-gating fusion layer dynamically prioritises informative modalities, while a class-balanced focal loss (beta = 0.999, gamma = 1.5) mitigates dataset imbalance without synthetic oversampling. Evaluated on 703 participants (570 PD, 133 healthy controls) from the Parkinson's Progression Markers Initiative using subject-wise five-fold cross-validation, SAFN achieves an accuracy of 0.98 plus or minus 0.02 and a PR-AUC of 1.00 plus or minus 0.00, outperforming established machine learning and deep learning baselines. Interpretability analysis shows a clinically coherent decision process, with approximately 60 percent of predictive weight assigned to clinical assessments, consistent with Movement Disorder Society diagnostic principles. SAFN provides a reproducible and transparent multimodal modelling paradigm for computational profiling of neurodegenerative disease.

翻译：刻画帕金森病（PD）的异质性临床表现需要将生物学与临床标志物整合到统一的预测框架中。尽管多模态数据提供了互补信息，但许多现有计算模型在可解释性、类别不平衡或高维影像与表格临床特征的有效融合方面存在不足。为解决这些局限性，我们提出了类别加权稀疏注意力融合网络（SAFN），一种用于鲁棒多模态分级的可解释深度学习框架。SAFN通过模态特异性编码器整合MRI皮层厚度、MRI体积测量、临床评估和人口统计学变量，并采用对称交叉注意力机制捕捉影像与临床表征间的非线性交互。一个稀疏约束的注意力门控融合层动态地优先处理信息丰富的模态，而类别平衡的焦点损失（beta = 0.999, gamma = 1.5）在不进行合成过采样的前提下缓解了数据集不平衡问题。在帕金森病进展标志物倡议计划的703名参与者（570名PD患者，133名健康对照）上采用受试者层面的五折交叉验证进行评估，SAFN取得了0.98 ± 0.02的准确率和1.00 ± 0.00的PR-AUC值，优于已有的机器学习和深度学习基线模型。可解释性分析显示了临床一致的决策过程，约60%的预测权重分配给了临床评估，这与运动障碍学会的诊断原则相符。SAFN为神经退行性疾病的计算分级提供了一个可复现且透明的多模态建模范式。