A Deep Learning Model for Prediction Alzheimer's disease Based on Microarray Gene Expression Data

Abstract

Alzheimer's disease (AD) is a major productive neurological illness with complicated genetic architecture. One of the main aims of biomedical research is to identify risk genes and then explain how these genes contribute to disease development. As a result, it is required to increase the list of genes linked to Alzheimer's disease. Genes play a crucial role in every biological activity. Microarray technology has given genes access to a large number of genes, allowing them to evaluate several levels of expression at the same time. Microarray datasets are categorized by a huge number of genes and small sample sizes. This reality is referred to as a multidimensional curse with a difficult task. A promising technology known as gene selection is addressing this issue and has the potential to revolutionize Alzheimer's disease diagnosis. In this work, gene selection approaches such as Singular Value Decomposition (SVD)