Alzheimer Analysis Using Machine Learning and Deep Learning Techniques

Mahule Roy

Abstract: This study explores the application of Machine Learning (ML) and Deep Learning (DL) architectures for the early detection of Alzheimer's disease. A comprehensive analysis is conducted using traditional ML models, including Support Vector Machine (SVM), Random Forest, and Logistic Regression, alongside advanced DL models employing Convolutional Neural Networks (CNN). Additionally, the performance of the widely used Inception method, a specialized CNN architecture, is investigated for comparative purposes. The study involves preprocessing diverse datasets comprising demographic information, genetic data, and neuroimaging scans. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are employed, utilizing cross-validation techniques to ensure robust results. The findings offer insights into the strengths and limitations of each model, shedding light on their applicability in Alzheimer's analysis. This research contributes to the growing body of knowledge on ML and DL applications in neurodegenerative disease detection, paving the way for enhanced diagnostic methodologies and intervention strategies.