Transformers and capsule networks vs classical ML on clinical data for alzheimer classification

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide. Although clinical examinations and neuroimaging are considered the diagnostic gold standard, their high cost, lengthy acquisition times, and limited accessibility underscore the need for alternative approaches. This study presents a rigorous comparative analysis of traditional machine learning (ML) algorithms and advanced deep learning (DL) architectures that that rely solely on structured clinical data, enabling early, scalable AD detection. We propose a novel hybrid model that integrates a convolutional neural networks (CNNs), DigitCapsule-Net, and a Transformer encoder to classify four disease stages—cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. Feature selection was carried out on the ADNI cohort with the Boruta algorithm, Elastic Net regularization, and information-gain ranking. To address class imbalance, we applied three oversampling techniques: synthetic minority oversampling technique (SMOTE), oversample using adaptive synthetic (ADASYN), and SMOTE-Tomek. In the three-class setting, the CNN + DigitCapsule-Net hybrid attained 90.58% accuracy, outperforming state-of-the-art baselines that rely only on clinical variables. A tuned gradient boosting (GB) model achieved comparable performance with substantially lower computational requirements. Model interpretability was assessed with SHAP and gradient-weighted class activation map (Grad-CAM), which identified Clinical Dementia Rating-Sum of Boxes (CRD-SB), Logical Memory-Delayed Recall Total Number of Story Units Recalled (LDELTOTAL), and Modified Preclinical Alzheimer Cognitive Composite with Trails B (mPACC-TrailsB) as the most informative clinical features. This combination of predictive strength, computational efficiency, and transparent interpretation positions the proposed approach as a promising open-source tool for facilitating early AD diagnosis in clinical settings.