Abstract:
Alzheimer's disease is the most prevalent form of
dementia with no established cure. Extensive research aims to
comprehend its underlying mechanisms. Genetic insights are
sought through gene expression data analysis, leveraging
computational and statistical techniques to identify risk-associated
genes. This study focuses on accurate AD detection using blood
gene expression data. Four feature classification methods—TFrelated
genes, Hub genes, CFG, and VAE are employed to identify
crucial AD-related genes. Five classification approaches—RF,
SVM, LR, L1-LR, and DNN—are used, evaluated by AUC. The
VAE + LR model yields the highest AUC (0.76). The study
identifies 100 influential AD-associated genes where data is
sourced from Alzheimer's Disease Neuroimaging Initiative
(ADNI). Findings hold promise for advancing early diagnosis and
treatment, enhancing AD patients' quality of life.
