ADxClass: Multi-Domain Attention Fusion and Imputation of Missing Heterogeneous Tabular Data

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive function. Accurate and early diagnosis of AD is crucial for effective management and treatment. Traditional machine learning models, though commonly applied, often fall short in capturing the intricate relationships between diverse tabular data. Furthermore, the missing data issue, typically addressed using conventional imputation techniques, leads to reduced accuracy and generalizability of AD classification models. This paper introduces ADxClass, a novel deep learning framework that enhances AD classification by leveraging multi-domain attention fusion and data type-based imputation techniques for handling missing heterogeneous tabular data. ADxClass integrates data from various domains, including demographic, cognitive, genetic, and biomarkers obtained from neuroimaging measurements, to improve the robustness and accuracy of AD classification models. The model's efficiency is validated via a 5-fold cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, showing significant improvements in classification performance compared to traditional machine learning approaches.