An Interpretable Deep Learning Approach for Alzheimer's Disease Diagnosis Using Gene Expression Data

With the global ageing population, the diagnosis of Alzheimer's disease (AD) has become an urgent public health priority. Gene expression techniques offer the advantages of being less invasive and cost-effective, but their high dimensionality and small sample sizes make them prone to the curse of dimensionality in AD diagnosis. This study proposes a novel interpretable deep learning approach to address these challenges. We introduce a shallow sparse autoencoder for dimensionality reduction and combine it with XGBoost for classification, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of up to 95.13% . Additionally, we develop a fast, low-cost feature selection algorithm that dynamically adjusts feature elimination to enhance model efficiency. Comprehensive cross-dataset evaluation demonstrates the model's strong generalisation performance on the public datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI), AddNeuroMed1 (ANM1), and ANM2. Our method also provides biological interpretability through enrichment analysis, offering insights into the mechanisms underlying AD and potential therapeutic targets. This makes our approach a promising tool for early, accurate diagnosis and clinical application.