Utilizing SVM, CNN to predict Alzheimer's disease

This abstract outlines a bioinformatics research endeavor focused on unraveling the intricate relationship between genes and age-related diseases, with a specific emphasis on Alzheimer's disease. The primary methodology involves the development of a classification model aimed at categorizing each gene, a fundamental step in understanding the genetic basis of diseases. To achieve this, this paper employed two distinct machine learning architectures: Support Vector Machine (SVM) and Convolutional Neural Network (CNN), both renowned for their efficacy in classification tasks. Prior to inputting data into these models, Principal Component Analysis (PCA) is utilized for feature extraction, a technique aimed at simplifying the dataset while retaining crucial information. This research endeavors to contribute to our understanding of Alzheimer's disease by leveraging advanced machine learning techniques to decipher the intricate genetic signatures associated with this debilitating condition.