NCA-EVA: An Innovative Ensemble-Based Approach for Alzheimer's Disease Detection from Magnetic Resonance Imaging

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder that is challenging to diagnose at an early stage. Affecting over 55 million people worldwide, its prevalence is expected to rise sharply by 2030. The use of artificial intelligence (AI) techniques has become increasingly important to improve the speed and accuracy of diagnosis. In this study, we propose the NCA-Enhanced Voting Algorithm for Alzheimer's Classification (NCA-EVA) to support computer-aided diagnosis. A total of 66 models were trained for four-class data and six models for two-class data. The proposed method successfully classified all four stages of Alzheimer's disease, achieving 98.97% accuracy in four-class classification and 99.87% accuracy in binary classification. Moreover, with a processing time of just 1.26 s, NCA-EVA is approximately 1200 times faster than a comparable study using NCA-based feature selection. These findings demonstrate that Alzheimer's diagnosis can be performed both quickly and with high accuracy, and the proposed approach has potential applications in other healthcare data analysis tasks.