Stacked Machine Learning Model for Predicting Alzheimer's Disease Based on Genetic Data

Alzheimer's disease is one of the brain disorders. It's also characterized as a degenerative disease because it becomes worse over time. Apolipoprotein E (APOE) is a genetic risk factor for Alzheimer's disease that has been linked to the disease in several genome-wide association studies (GWAS). Single nucleotide polymorphisms are the most common type of genetic variation among individuals (SNPs). SNPs have been identified as important biomarkers for this condition. SNPs aid in the study and detection of the disease in its early stages. We focus on employing a stacked Machine Learning (ML) model to categories Alzheimer's patients in this paper. The model was tested on all AD genetic data from phase 1 of the neuroimaging project (ADNI-1). The results showed that the stacked model outperformed other machine learning methods with an overall accuracy of 93.7 percent. The findings suggest that stacking approaches are effective in detecting Alzheimer's disease.