MAFNet is an end-to-end deep‑learning pipeline that integrates temporal denoising, swarm-based feature selection, and graph‑attention multimodal fusion to stage Parkinson's disease from SNPs, neuroimaging, and UPDRS data, reporting 97.6% accuracy on PPMI (n=200) and nominating LRRK2 variants,…

The method has strong translational potential for biomarker-driven patient stratification and real‑time clinical staging to support precision trials and therapeutic decision‑making, but its high reported performance and biomarker claims need replication in larger, independent cohorts before…

Parkinson's disease (PD) affects 10 million globally, with accurate staging essential for personalized treatment planning. Current UPDRS assessments achieve < 93% accuracy due to subjective clinical judgment and unimodal data limitations, failing to capture complex genetic-neuroimaging-clinical interactions driving disease heterogeneity. This study introduces MAFNet, a novel deep learning framework pioneering Iterative Adaptive Vold-Kalman Filter (IAVKF) temporal denoising, Accelerated Binary Particle Swarm Optimization (ABPSO) swarm feature selection, Multilayer Perceptron-Lagrangian Support Vector Machine (MLP-LSVM) classification, and Graph-Attention Based Multimodal Fusion Network (GAMF). Applied to PPMI cohort (200 patients) with genetic SNPs (50), neuroimaging voxels (1,024), and UPDRS-III scores, the end-to-end pipeline delivers 97.6% accuracy, 98.2% precision, 96.8% recall, and 97.3% F1-score-outperforming CNN (92.4%), Autoencoder (90.8%), InceptoFormer (96.6%), and HCT (97.0%). IAVKF boosts SNR + 15.2dB (+ 2.9% accuracy vs. PCA/t-SNE); ABPSO reduces 1,276→340 features (73% reduction); regularization cuts overfitting gap to 0.9% (vs. 4.2% baseline). SHAP interpretability validates clinical plausibility (top predictors: LRRK2 SNPs, UPDRS-III tremor, hippocampal volume). Five-fold CV confirms stability with the Indian cohort external validation. Real-time inference (0.2s/patient, RTX 3090) enables clinical deployment. Future scope includes longitudinal temporal modelling, modality-agnostic fusion, edge deployment, federated learning, and extension to Alzheimer's/ALS. MAFNet transforms PD staging from subjective assessments to objective precision medicine, enabling biomarker discovery, progression forecasting, and personalized therapies across diverse global populations.