Stacking-Based Machine Learning Approach for Alzheimer's Disease Diagnosis

Abstract

This study investigates the use of machine learning models for diagnosing Alzheimer's disease, based on the Alzheimer's Disease Dataset from Kaggle. The dataset includes a variety of features that are crucial for identifying Alzheimer's in patients. We applied different models such as Random Forest, Support Vector Machine (SVM), XGBoost, and a Stacking Model to predict the disease's presence. After evaluating the performance of each model, the Stacking Model emerged as the most accurate, achieving an impressive accuracy rate of 96%. This result demonstrates the potential of combining multiple models to improve diagnostic accuracy in Alzheimer's disease detection.