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Examing and Evaluating Dimension Reduction Algorithms for Classifying Alzheimer’s Diseases using Gene Expression Data

Alzheimer’s disease (AD) is a neurodegenerative disease. Its condition is irreversible and ultimately fatal. Researchers have been studying approaches to support early diagnosis of Alzheimer disease and further delay the patient’s condition and improve AD patient’s quality of life. Gene expression data is a mature technology. It has many advantages such as high throughput, less-invasiveness, and affordability. It has great potential to help people diagnose Alzheimer’s disease in early stage. However, because the amount of information is too large compared to the number of samples in the Alzheimer’s database, researchers are facing “curse of dimensionality” when using gene expression data. In this work we are interested in the task of dimensionality reduction of gene expression data in Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We investigated six dimensionality reduction algorithms: Principal component analysis, Kernel principal component analysis, Isometric feature mapping, Local linear embedding, Stacked denoising autoencoder, and Stacked sparse autoencoder. An SVM classifier is used to classify the samples in the ADNI dataset using the features obtained by dimensionality reduction. We first optimized the appropriate number of hidden layers for the two stacked autoencoders. Then we performed different degrees of dimensionality reduction with the other four algorithms and compared the classification performance of the features obtained by different algorithms with the SVM classifier. Our experimental results show that the features obtained by kernel principal component analysis and local linear embedding, Stacked denoising autoencoder, and Stacked sparse autoencoder dimensionality reduction have good AD classification performance.