Multi-modal Data Fusion with Missing Data Handling for Mild Cognitive Impairment Progression Prediction

Predicting Mild Cognitive Impairment (MCI) progression, an early stage of Alzheimer’s Disease (AD), is crucial but challenging due to the disease’s complexity. Integrating diverse data sources like clinical assessments and neuroimaging poses hurdles, particularly with data preprocessing and handling missing data. When data is missing, it can introduce uncertainty and reduce the effectiveness of statistical models. Moreover, ignoring missing data or handling it improperly can distort results and compromise the validity of research findings. In this paper, we introduce a novel fusion model considering missing data handling for early diagnosis of AD. This includes a novel image-to-graphical representation module that considers the heterogeneity of brain anatomy, and a missing data compensation module. In the image-to-graphical representation module, we construct a subject-specific graph representing the connectivity among 100 brain regions derived from structural MRI, incorporating the feature maps extracted by segmentation network into the node features. We also propose a novel multi-head dynamic graph convolution network to further extract graphical features. In the missing data compensation module, a self-supervised model is designed to compensate for partially missing information, alongside a latent-space transfer model tailored for cases where tabular data is completely missing. Experimental results on ADNI dataset with 696 subjects demonstrate the superiority of our proposed method over existing state-of-the-art methods. Our method achieves a balanced accuracy of 92.79% on clinical data with partially missing cases and an impressive 92.35% even without clinical data input.