Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

Alzheimer's is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode and reveal Alzheimer's. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised-based method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper based Particle Swarm Optimization (WPSO) and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer's dataset from the Gene Expression Omnibus (GEO). We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyper parameters in the IDBN. The tabulated results show that the proposed pipeline shows promising results.