Objective : Alzheimer’s disease (AD) continues to be a major challenge because handling high-dimensional data is time-consuming and expensive due to its complexity. A large feature space often increases computational costs and reduces model interpretability. This study addresses this problem by evaluating and comparing multiple feature selection techniques to identify the most informative biomarkers for AD diagnosis. Methods : Our study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to implement and test three feature selection approaches, visualization-based, filter-based, and wrapper-based, within a Naive Bayes (NB) classification framework. Results : Based on the results of the analysis, the wrapper method achieved 96.77% classification accuracy, outperforming both visualization and filter methods with 86.19 and 91.87%, respectively. Interestingly, even when over 92.5% of the original features were removed the classifier still performed well, indicating that only a small set of features is necessary to ensure reliable diagnosis. Discussion : This study illustrates that strategically selecting features improves diagnostic accuracy while reducing computational burden, providing a more efficient framework for machine learning applications in Alzheimer's disease research.
Masood et al. (2026) studied this question.