Machine Learning-Based Fusion of MRI and Transcriptomic Features for Alzheimer’s Disease Diagnosis
Keywords:
Alzheimer's Disease, Machine Learning, MRI, Transcriptomics, Multimodal Fusion, BiomarkersAbstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder requiring accurate early diagnosis for effective intervention. While MRI provides structural brain information and transcriptomics offers molecular insights, their integration remains underexplored. This study presents a novel machine learning framework that fuses MRI-derived features with blood-based gene expression data for enhanced AD diagnosis. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we extracted volumetric features from T1-weighted MRI scans and identified differentially expressed genes from peripheral blood transcriptomic profiles. Feature selection employed SHAP values, and class imbalance was addressed using Borderline-SMOTE. Multiple classifiers including Random Forest, XGBoost, and Support Vector Machines were evaluated using cross-validation. The fused feature approach achieved superior performance (accuracy: 92.4%, AUC: 0.96) compared to MRI-only (86.7%, AUC: 0.91) and transcriptomic-only (83.2%, AUC: 0.89) models. Key biomarkers included hippocampal volume, BDNF expression, and APOE-related genes. Feature importance analysis revealed complementary information from both modalities. Multimodal fusion of MRI and transcriptomic data significantly improves AD diagnostic accuracy, supporting the paradigm shift toward integrated biomarker approaches in neurodegenerative disease assessment.
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